 Okay, we're back inside theCUBE. This is SiliconANGLE.com's continuous exclusive coverage of Strata plus a dupe world. This is theCUBE, our flagship program. We go out to the events, extract the signal from the noise. John Furrier, the founder of SiliconANGLE.com, joins me with my co-host Dave Vellante, wikibond.org. SiliconANGLE is the place to get all the signal from the noise. Wikibonds, we get the free research, we're our research analyst firms. And we're excited to have ClearStory CEO, Charmelia Mulligan of ClearStory. Welcome to theCUBE. We have a big famous keynote yesterday. Thank you. Welcome. It's great to be here. So ClearStory kind of coming out, new startup. That's right. I didn't actually get the official funding numbers. So did you guys disclose that? We did not disclose that. Did you disclose the investors? We sure did. Our investors are Andreessen Horowitz, Google Ventures, and Coastal Ventures. Okay, okay, so you did announce that. Okay, so from what I heard, good size round in a hot market. You gave the keynote yesterday. We were watching from the balcony because it was so packed. But you had a really good message and I want to drill into one, the company that you have, and then some of the value propositions that you guys are attacking, and specifically around the innovation and disruption in the marketplace. So first, tell the audience about the company that you founded and what you guys are doing, where you are, obviously funding. Right, so first thanks for having me here. ClearStory data is very focused on what we call the last mile problem in data analysis, which is about making it easy for business users and business analysts to be able to consume data from diverse sources. And as you look at all the sources of data available today, it goes beyond just your private legacy repositories of data and extends to a lot of new big data platforms, many of which everyone's heard about at this couple day event here at Strato. But the net of all that is there's this explosive growth across all your private data that is now housed in many different sources, legacy sources, new platforms. But in addition to that, there's also a rich number of sources of external data that is available to tap into. And in fact, there are about 7,000 open data APIs available today that range from sources like Twitter, Facebook, Google public data, Netflix, Best Buy, so on so forth, that are open APIs that allow you to actually extract data from a wealth of other sources. And the real opportunity here for customers is to be able to bring together data from multiple private sources, including the new big data platforms and their legacy platforms, and fuse that with data from public sources that comes from a whole host of different web sources and premium sources available today. And the number of these external sources are going to continue to grow. So we're only at the beginning of the wealth of data available through all those external sources as well. The opportunity here and what ClearStories focused on is giving you a way to actually bring this together in a very easy to use platform and also making it possible for anyone to actually access data and data at scale and diversity of data and run a analysis that doesn't require them to be highly technical in terms of their skillset. So mass consumption of data is the way we like to think about what ClearStory is doing. Other people like to call it a new platform for democratization of data, but call it what you want. The net of it is we want to ease data consumption when looking at data from both private sources and external sources at scale. So you mentioned a couple of things here, open data, APIs. I mean, this is where the movement's going. Clearly mashups of data, people call it different things, the classification of data, pick a buzzword, but basically it's mashing up multiple data sets and doing that really fast, elegant way. In your keynote, you talked about private and public data. Let's drill down on that because that seems to put a lot of different requirements on the old way of doing things. So back in the old days of data warehousing business intelligence, take us through that old way and new way and what's different and what are you guys looking to disrupt? Yeah, so the old way actually relies on primarily relational sources of data with a known or constrained data model and the tools that emerge to make it possible for data architects and data analysts to derive data out of those systems were also designed with that type of constrained model in mind. I mean, if you look at like the last several decades of tools that emerge that sit on top of the legacy of relational sources, they were designed the way they were designed because it came from the constraints of the data model that the sources presented. Where we are today is that the sources of data have changed in terms of the type of model that's now available. So what we are focused on is actually allowing you to work with more of an open data model versus a very constrained data model which is what we had, we're dealing with at the last 15, 20 years. And when you look at the new external sources of data, they come in a variety of different data types, different data structures. And so the openness of the data model becomes very important and the tools and platforms that now allow you to consume data out of those sources have to be more flexible in nature. So flexibility has become very important as it relates to how you actually design the new tools and the platforms that will sit on top of these sources versus the way you did it before which is through a very sort of constrained and structured approach. So how do I engage with ClearStory? How you engage? Well, we are currently as a company, we're still in our early access period. So if you want to engage with ClearStory, you can come to clearstorydata.com and you can sign up for our early access. Eventually how you engage with ClearStory when we fully go to market is we have a variety of different mechanisms by which our product is going to be available, both direct and indirect. And so there'll be very broad market reach and accessibility to the product. So it'll be a service that I can then access and customize for my own needs. That's right. So you will be able to log on to a service and be able to get going with the data that you want to work with. So one of the complaints of Hadoop right now is we just had a practitioner on who's saying, she loves the cloud but the tools and the tooling up of Hadoop's not there yet. That's right. Because the business value of the data is obviously critically want that but she can't spin up all the expertise, right? So that's one factor. So the question I have for you is what are you guys doing specifically around Hadoop? Is it just Hadoop solutions and other databases? Are you guys sitting on top? What's the ClearStory product? Yeah, we work with a diversity of backends and diversity of sources. The legacy relational sources are just as important as the new Hadoop platforms which are just as important as the rich sources of external data now available. We're not necessarily biased towards one or the other category of source. It's about what the customer is trying to do today which is derive data from multiple sources wherever they may choose to house that data and wherever that data may live. That data may live in a whole variety of external sources that are not sources they own but sources that they need to tap into or it may be data sources or platforms they do own whether it be their traditional relational sources or their new Hadoop platforms. So regardless of where your data lives whether it's external, internal and the nature of that internal platform our goal is to make it easy for you to consume data out of those platforms and make it very easy for the non-technical user so that someone who's not necessarily got a very highly skilled data analyst or a data architect to be able to actually access data that they couldn't access before. So easing access to data and making it very easy to get to and very easy to combine when you bring it together from multiple sources and then being able to explore that data. It's truly non-technical. Truly non-technical. It doesn't mean that technical and data architects and data analysts won't use Cloj story. They absolutely will because it's got the power that they need in terms of how they want to work with data but we've gone beyond that which is to make it easy for non-technical users as well. And that's why we talk about mass consumption because that's truly what we're after. We were just at, first of all we cover this area pretty heavily and we were just at the information on demand event in Vegas. I took the red eye, Dave took the red eye the next day. But this idea of data DNA is constant that Dave and I were talking about is that if you don't know where the data came from you really don't know how real it is. There's like a whole another element of kind of thinking around that. And the second thing that's coming up that we're covering on theCUBE here is the humanization of factor meaning and I think you touched on this new keynote about human intelligence is huge and that the people part of the equation is massively important and that some geeks overlook. So I want to explore that with you for a second. So tell us your view because you have a lot of history with the data warehouse, the old school and now the new school. Given all the mashups and the human component, how important do you think that is and give some examples of what you think that's changing the equation for. In other words, what's the role of people and why it's changing? So great question. So we believe that as users are presented with more data as data explodes in size as well as data grows in terms of the diversity of data you have to be able to aid what I call aid human insight. So the goal of new tools that are focused on data consumption out of these sources and data analysis have to be able to guide the user on what this data really means and what the value of the data is. So we talk about aiding human insight, we talk about amplifying human intelligence. We do believe that you cannot leave it entirely up to a user to figure out what all this data means but there is a role here for technology to play and to actually aid in the process of them understanding what it means. So progressively taking them through the data and showing them every step of the way what they can do with that data. So that's one way you aid human insight but there's many different approaches to how you actually aid human insight in the process of pulling data from multiple sources and dealing with it at scale. The other aspect of this is that you need to make sure that your users actually understand and can get to a way to understand what the result really means and whether they've actually arrived at the result or there's more to explore. And so that's another element of what ClearStory thinks about and what ClearStory brings is, what you might think is the right result out of a certain insight may not necessarily be the complete result or the complete insight. There may be an opportunity to explore even more. So how do you design that into the product? We just had Ben Werther on from platform and we had a great conversation because he's got some disruption going on with his old deal with BI with no ETL and data warehouse required which is a great bumper sticker. That's a total geek bumper sticker. So okay, you got schema problems. So to do that kind of flexibility you need to have a system. So how did you guys, what's the secret sauce to your product that's going to make the human? Who's like, to me like query and Google. It should be that easy. Guys, you don't have to chain schema to ask a different question. I would like to reserve until we're actually rolling out the product fully and I think you'll see. And I think I hope you'll be like query. Can you show a little bit of a, but we've blown away, is that? I hope you're blown away. I hope to blow everyone away. Can you get a little bread crumb, feed us a little cracker, nugget of insight? Now, I'll speak a little while. When we're ready to do that we will give you that little nugget. But the key here is, and like I'm saying is we do believe that it's not just about throwing new tools and technology to the user. There is more to it than that. Simplification is incredible. And simplification means very different things today than it meant 10 years ago. And simplification when it comes to data at scale and diversity of data again means very different things than what it used to mean. So there is a huge amount of opportunity here, we know it well, because we're doing it at ClearStory to make things a lot more meaningful and easier for the user who doesn't necessarily have a data scientist type of skill set. And that can be done and that is the focus of the company. And we're in the heart of it and doing it and I think we're doing it pretty well. And as we unveil more of this I think you'll see. But there's a big aspect of this where we don't believe that everything can be done by the tool itself. The tool has to aid in the process of an insight and get smarter in terms of how it aids in that process. So another question you might not answer, but I'm trying to, you talked about 7,000 open data APIs and you showed a graph, a keynote of this hyperbolic curve, exponential curve. And so am I to infer that there's more than just a pathway to those APIs and some IP around? Oh, absolutely. And out of the 7,000 data APIs there are a whole host of them that masses and masses of customers want to be able to access. And there are many of them that are very niche, right? But regardless, there is a massive amount of rich data emerging behind all of those open data APIs and just the growth of them is phenomenal. And we are basically, want to think uniquely actually coming at that problem with also recognizing the growth of data in your private data repositories who do include it. And I think that's what makes Clear Story very unique. It's the recognition that not all valuable data lives inside of the organization, that there is a whole amount of valuable data that is emerging outside of the organization. And there's a massive appetite from customers to be able to go find that, even know where it is. I mean, it's not easy to go scour 7,000 APIs. So they need to understand where the value is and be pointed to where the value is. Well, what I like about what you're saying is it's also a recognition that you can't predict what data sources are going to be available in the future. That's right. So you're agnostic to those data sources and building a capability to accommodate them. That's right. Whatever they are. So you need to build the capability to accommodate what's available now, to accommodate what you don't even know about that might be coming down the line here. And you can see the rapid growth of the Open Data APIs. You're just going to see massive amounts of them emerge. So building a platform that knows and has the smarts to actually deal with all of that and has the right architecture to handle it is very important. And recognizing that customers have a huge need to bring that together with the explosive amount of private data is becoming basically a big opportunity out there. And that's what we are focused on. But throwing all this data out to users is not out the answer either, right? Like throwing out 7,000 Open Data APIs and many, many like of your internal platforms and allowing consumption of data out of all of these sources is not the answer either. Because again, you're just then going to throw too much complexity onto your screen. So how do you actually take all that and make it simple enough where it becomes a highly intuitive process and easy for a non-technical user to manage through that is what we are after? So I want to shift gears. I mean, I'll talk about you. Okay. So this is your first time on theCUBE so we got to get to know you. Tell us your background. I know you know my friend Greg Sands, great guy, friend, but tell us about your background, where you've been in your career and then I'll ask you a couple of pointed questions. Sure. So most recently was early days of a company called Astadata that was a big data platform. It's now part of Teradata as a result of the Teradata acquisition last spring. So spent a lot of time in the big data area through Astadata. Prior to that, spent some early days at Cloudera when the Cloudera team was first pulled together. Previous to that, did a company called Opsware was on the executive management team for Opsware which was acquired by Heald Packard for 1.65 billion, about four and a half years into doing Opsware which is where I got together with Mark Andreessen and Ben Horwitz. Prior to Opsware was at Netscape, so early days at Netscape and I arrived at Netscape through a company that we put together three of us to build basically the first application server that was called Kiva Software. That's right, yeah. One of those days. 97. Well, you got it. Thank you, show my age. Kiva Software was acquired in 1997 by Netscape which is how I arrived at Netscape. I was VP of Infrastructure Products at Netscape and following that, landed at AOL through that acquisition and then did Opsware with Andreessen and Horwitz and big data over the last five years. So I wanted to get that out because I want the audience to know how impressive you are and I knew your background but really my next question is to build on that. You've seen cycles of innovation. You've obviously, through the key vac, at the time was pretty cutting-edge stuff and Netscape was doing some cutting-edge stuff. Even at the time, all basically found that the web is well-documented. But as you went through your career, you ended up at the end prior to this, Opsware, first cloud company. Ben, Mark, Insuk, all those guys were doing some great cloud for his cloud when he has all bare metal cloud. But now it's different. So I want you to compare the evolution and kind of what's different right now. Why is right now, moment in time, so transformative? Why now? What's different about now? Even vis-a-vis client server, web client server and PC revolution. What's so transformative about this so-called big data or whatever it morphs into? Yeah, I think it's a good question. What I've mostly spent my career in is shaping new markets. We was at the beginnings of shaping the application server market when there was nothing available but web sovers. There was no way to do a transaction over the internet, which is why we created Kiva Software. It led to a multi-billion dollar market of application sovers, B.A. entered and many others. I saw through the shaping of the internet, from the early days of the browser to all the internet infrastructure that was created at Netscape. We at Opsware then went off to actually help manage all that infrastructure. I mean, Laotard and Opsware was created to go manage the problem that we actually were the source of at Netscape, which is we created all this infrastructure and threw it into data centers and now we had to build a platform to go manage it. So we created the data center automation market. So Dr. Pugner, as you folks, they solve problems, create other problems, go solve those problems. That's the roadmap. Yeah, you're part of that pioneer class, Mark Andreessen, you know, people doing stuff before was cool. That's right. So with every step of this, if you look back, at least to my career in early 90s, each time we created a new market, but we also following that created a problem, right, that had to be solved. So what I see right now with big data is we're in a similar phenomena. We've actually created a whole lot of new applications that throw off so much data that we are now in an era of, you know, having to go solve the problem of how do you manage all this data. But I find really unique about the big data market versus some of the other markets I've done in the past and seen evolved is that in this case, it's not technology creation for the sake of technology creation. It is a dire urgent problem out there that customers are struggling with and we almost cannot like, can't seem to solve this problem fast enough, right? The demand is almost like outpacing the technologies that are available today to go address the problem. So with or without the number of new companies you see in the big data space, the big data phenomena is happening, right? And it's being created by the explosion of data that's coming out of the new applications that organizations are now using and someone has to go solve that problem. Machines, internet of things. You got it. Machines throwing off more data, consumer applications throwing off more data, mobile applications throwing off more data, SaaS based applications throwing off more data. We have basically created this problem inside of organizations and it's our job to go build technologies to go solve it. I think, first of all, 100% agree with you. I think that this transformation is more powerful than a PC revolution and client server combined and it's happening faster. So it is a true call to arms for geeks to solve problems just like building bridges. I was talking to my friend, I'm like, you know, back in the day you got an engineer, you build bridges. Now we've got to build tech to solve our data problem and totally agree with you. So it's not just like, and Jeff Hammerbach had the epic quote, you know, that our brightest minds are spending their machine learning and skill, making ads more efficient to click on versus actually solving real problems. So, you know, the data problem is happening with or without us. It's going to keep getting worse. It's an opportunity. And either we go and find ways to solve it or it just keeps getting worse out there, right? And so this is a unique time. I mean, I don't, I think that the set of companies in the space actually are in a much more unique position than even what I saw over the last four or five markets have seen evolve in that in some of those cases we created a technology and the market followed. In this case, the market is already there. I mean, it is ripe. People are in a lot of pain. Customers are in a lot of pain and they are hungry for companies like ourselves to go build technology to solve the problem. Yeah, that's why the whole Civil War thing about Hadoop's a non-issue. That's why IBM is embracing Hadoop. That's why HP's here. That's why the big companies are here because there's so much work to be done and so much beachhead of opportunity and wealth creation. So entrepreneurs, get out there and get cracking. Forget that Web 2.0 app and get cracking on the big data problem. Shimela, thanks so much. You're a total tech athlete. Love your background. This is theCUBE. We extract a signal from the noise and then there it is right there. Clear story out to make the stories clear with the data, making sense of the data. That's the top story here at Strata. Insights, analytics, the clear story and also handling the tsunami of data around business value. This is theCUBE. We'll be right back with our next guest after this short break. Thank you. That was great. Thank you very much. All right. 18 months ago, we set out on really revolutionized...