 here at Stanford University in Palo Alto, California. Actually, Stanford, California, it's its own town. I'm John Furrier, the founder of SiliconANGLE, and this is theCUBE, our flagship program. We go out to the events, extract a silver noise. I'm joined by my co-host, Jeff Kelly, this week, and Oprah Cahane, who's origami logic. You guys are stealth mode? Very stealthy. You can't see my face, but we've revealed that now. Welcome to theCUBE. So you can just watch the microphone there. It's a little noisy now, the session's getting out. I just saw Kara Schwisher from All Things D, Bud Collig, and all the luminaries are here, all the top Excel partners are here, and all the startups, you guys are startups. So talk a little bit about why you're here, and what are you guys talking about in the hallways? You haven't launched yet, or what's the deal? Yeah, so we're still very stealthy, haven't launched yet, so I can reveal a whole lot about what we do. Happy, of course, to talk about the things that we solve, the problem space that we tackle. And we're here, as this is certainly the event to be, we're one of Excel's recent investments in the big data space, and all this. How much did they put in? We raised it to around $9.3 million late last fall. Series A? Series A, with Excel being the primary investor. Awesome. So talk about the big data, what problem are you guys solving, what's the big challenge? So the challenge we're solving is how big data gets really applied to help marketing folks. Marketers are really struggling with the fact that they're surrounded by this ever-increasing number of customer touch points. Each touch point typically comes with some marketing system to manage the content, the workflow, the interaction with the audience across that channel. Whether it is web in the early days of digital marketing, down to advertising, search, social, which is now an explosion of the plethora of different types of channels and apps. And the more innovation we feed here in Silicon Valley and River Elf, driven by the axels of the world in funding, a lot of those next-gen marketing platform of social interaction platforms, the more touch points there are, the more data is generated by these systems, and the more complex is a marketer's world and around how do they touch their customers, where should they touch their customers, how can they make sense of the marketing landscape in and around them. And that complexity is not really effectively tackled by any existing platform tool. Thankfully, the underlying big data platform, such as the ones coming from Cloudera and Friends, are helping deal with how does one store the data, how does one process the data at scale. The next phase beyond that of verticalized big data apps is one where by this, how do business people actually monetize the data? And we're a perfect example of how does one apply that trend to marketing. We're building really, think of it as Splunk for marketers or Mint for marketers, that centralized cloud-based platform that lets folks in a marketing organization without IT involved, without data scientists involved, have all the data matters to them automatically collected into this big cloud-based platform, automatically integrated, because integration of data in any environment, and certainly in the marketing environment, which is super siloed, is extremely complex and difficult. So we apply data science to automatically integrate the data sets, and then provide beautiful visual marketer-optimized user experience for marketers to directly interact with the data and make sense of the world in and around them. So I definitely want to get into some of the issues around the challenges facing marketers and how they do their jobs. But I wonder if you could talk a little bit about, as an entrepreneur, you mentioned how the infrastructure layer where we're storing processing data is starting to mature. Talk about as an entrepreneur where the opportunity for yourself and others to actually start taking advantage of that infrastructure. Is it a lot easier today to start building a big data application than it was a year ago, two years ago? Talk about that transition and what it means as an entrepreneur with an idea for a big data application to actually have that underlying infrastructure and platform, whether it's Sadoop or something else, available to you and how that kind of makes it possible to actually turn these apps into reality. Yeah, so I think we are getting to that point that it's a whole lot easier to develop the application layer, riding it up with a platform. I'll say, much easier than it was a year ago, it's still a lot more difficult than it will be two or three years ahead as the platform is still evolving. When I say platform, I talk about the platform at large, Hadoop and the ecosystem and any of the other no-sequel and big data building blocks. There's still complexity around tying the individual pieces together into your underlying data platform, but that is getting less and less difficult over time. So it appears to me that this is a perfect time for entrepreneurs to think about not just what is that next potential piece that's missing at the underlying infrastructure layer. I think that's heavily crowded. A lot of this was started in place. The big players and big bits have been made. It's more about how do you help businesses or consumers depending on the nature of the application, monetize the value of the data. And as an entrepreneur, you don't need to think as much about solving the very fundamental basic computational and storage challenges that Hadoops of the world helped us all out of the box. But rather, what is the specific business workflow you can help tackle? Or the consumer need you can help tackle? And focus on that, and I think that's where a lot of the next generation value will come from big data or the next step in the evolution of the industry. Absolutely. Now the conversation is starting to turn to or include how do you monetize that data that all the players like Flutterers and Hortonworks and others of the world are actually helping you store a process. Now it's about entrepreneurs like yourselves building on top of those platforms and actually turning that data into something that can mean a business value. Exactly. So you're taking a kind of a function specific look at at the way you build your application who you're targeting marketers in your case. I wonder if you could talk about why you took that approach as opposed to building a more horizontal platform with visualizations on top of big data for maybe not just marketers with sales or finance or whatever the case may be. Why that specific focus on marketers or a specific domain? Yeah, so I think there are two questions in one here. First, why marketing? Our sense after thinking a lot about the application layer from an entrepreneurial point of view was that marketers are uniquely positioned in having and being really data and budget rich. They truly strategically impact the success of the companies they drive. On the other hand, they are skill and insight poor. Skill because to monetize the value from that data richness requires a lot of complexity, a lot of data science which does not exist in most organizations neither the IT nor the marketing department. Secondly, we felt that the value that we could provide in solving these pain points would be one which will have impact on the top line of companies versus potentially dealing with other potential verticals. Though functionally speaking, there are similar problems I'm sure that can be solved for any of the other functional departments that you've mentioned whether it's finance and are companies chasing that or whether it is sales or lots of companies chasing that and so on and so forth. Now, why vertically focus in this case functionally focus versus purely horizontal? There's another underlying theme here which I think is important and my bet is we'll see more and more of that over time. The complexity of any of the functional datasets marketing, sales, finance and so on and so forth is becoming so high and surely the marketing space given the data being so siloed and diffused and heterogeneous and in variety and form that makes any of the generic tools inherently ill at that and incapable to truly embrace the complexity of what needs to be solved in a way that is immediately valuable to the marketer and the business. You need to apply lots and lots of data science in order to automatically fuse those datasets. You need to apply lots and lots of data specific connector development to collect the data which is very complex in its form and normalize it into something that would be useful specifically for marketers. And certainly so the user experience I believe over time will end up being more and more verticalized in its value such that if you're building something for marketers just taking the generic ways to visualize data won't work. You need to visualize data in terms that are specific to marketing data types and specific to the marketing business processes which are different from sales or engineering or logistics or whatever the case is. Stay differently. Our thesis of over time generic BI while will be useful for certain use cases will diminish in value given the complexity of the data specific and function specific technology you need to slap on top of it in order for it to be useful out of the box for your business users. So my question to you is obviously you can't talk a lot about your company right now but talk about just as an industry participant what are the big misconceptions of big data? Is it easier than people think? Is it harder than people think? Obviously it's pretty hyped up right now even here at Excel where they're considered at the front edge of the world and they talk about consumerization of IT like it's a new thing. So you have this shadow IT, you have consumerization happening, been around for a while. What do you share about big data that you could clear up or just comment on? So a few different thoughts. First and foremost, the hype is certainly broad. I think over time the changes of big data is driving will be as massive and impactful on the entire industry as the evolution of the early relational database has been to enterprise software 20, 30 years ago. That being said, when folks use a term big data a lot of the complexities behind that are actually being obscured. First and foremost, it's not just about the size, it's about the variety of the data and a lot of folks seem to be missing that point. Another super critical point is the human aspect of it or the organizational aspect of it. In many organizations to really get to that optimal point where you can leverage the data in big data terms, you need to really break down silos which are not just technological in nature or rather organizational in nature. For example, when if you use CRM data with social data, with product inventory data and so on and so forth, it's not just a technical problem that's really difficult to tackle but rather organizationally, how do you get access to all these datasets? Who owns that within the organization and so on and so forth? In my earlier career, I've been more in the networking space and used to speak about the networking stack, layer one through layer seven and there are always difficult technology challenges there. You used to typically joke that the most problematic challenges are actually layer eight, the human layer, the organizational layer and I think it supplies to here in big data. Final point to be made, ultimately the value that folks drive out of big data, manifests itself in small data. It's those pieces of data within this massive haystack of silo data you mesh together that really matter and ultimately it's about the individual business user, how fast can they consume the value from that data, how fast can they find those nuggets of value that drive action and insights that drives ultimately some monetization versus some theoretical concept of here's value in the data. You know, we've seen that too. I was one of the first to co-intern fast data implying little data, because it moves around a lot, it's loose data, whatever you want to call it, but I would agree with you there. But I want to go back to some of the technology issues because even though layer eight, the human layer is challenging with mobile, which throws off a lot of small data. Gesture data, gestures here and there. But you mentioned relational databases. The database is a big part of the stack now where you have solidified networking, moving off that concept of virtualization now, in Naveler. What is the future of these stacks? There's stacks out there, there's technology stacks, and the database in particular. Can you comment and just give some color to, you know, which is once a element, hit the database, get it returned? Now you have a variety of data. So database got to fit into these stacks, a lot of multiple stacks, little dimensional issues. Can you comment on this whole database? Absolutely, so I think when folks talk about database in legacy terms, typically there was the relational database, a single source of truth, data warehouses, you know, dangling it around it and so on and so forth. But folks spoke about the database, and data was always pretty rigid and stuck into that template. I think reality of modern data is first and foremost super, super siloed, super fragmented, super diffused, so it's no longer a database. It's many, many different data repositories that you need to suck the data from in order to drive value. Second point is the data is so varied in form. Unstructured data, structured data, temporal data, geospatial data, relationship data, graph data, all these things that certainly in our world marketers need to face, and traditionally they would need to use different tools to analyze different aspects. It takes analysis system to analyze unstructured takes from social. Some graph mapping software to figure out relationships between people and product and networks and whatever else, and so on and so forth. So I think we'll just see this plethora of more and more data types that need different special tools for them. And complexity ultimately will need to be dealt with, something we're looking at from a stack perspective by higher abstraction layers that help hide that complexity under something that's useful for a bitters person that doesn't need to and shouldn't care about whether some of the data is stored in one form to tackle unstructured data and some other form in order to tackle relationship or temporal or any other form of data set. So NetNet no longer the database, but many different types of data stores or different types of specialized form of data will need to be hidden at the application layer. Yeah, I think you're right on there. A business user doesn't care when they have a query or exploring data via visualizations if it's a relational database working into the covers or a dupe or anything else. It's just they want an answer to their question. They have a business problem, they want to solve it. They're not necessarily care about the underlying infrastructure. I want to go back though, you mentioned kind of the human problem and I was actually at the SAS Global Forum yesterday and there was a customer panel and one of the gentlemen there was a workshop financial services firm and he said, we have some long time employees, people who have over 30 years at this company. And he said, one of the biggest issues is of course the changing the way they think and do their job when it comes to using data to actually make decisions. Now of course, now SAS, interesting enough is one of those kind of more horizontal plays and they've got a big BI player which I'm sure you're quite aware of. But I wonder how do you, if you're talking to a CMO and they're saying, look, this sounds like a great way to get better insights into our business, but how do I go about convincing and all of my employees who've been doing things a certain way, getting answers a certain way to actually start adopting these new approaches and do it in a way that doesn't kind of, you don't want to go in and say, the analytics knows better than you. You want to go in and say, this is going to kind of complement what you do and what you know. How do you have that conversation and what are some tips for CMOs to make that transition with their employees? So I think it's a super critical point you're making and I totally agree with it. Ultimately, folks shouldn't have that presumption that big data is this kind of state that solves all of their marketing problems. It's a tool, it's just a means rather than the end. And in our mind, it's ultimately about bridging the value you can extract from data through automated technologies and big data infrastructure with intuition, certainly in the marketing space which is still critical because no machine, no machine learning engine can truly empathize with the consumer and the consumer journey that's being made as a marketer can with their intuition because there's a lot of emotion that's involved with Bridges process. Hence, at least the way we're taking this from a product development perspective and a market perspective, we're thinking about our solution is a way that helps bridge human intuition with underlying big data. First thing, second thing, when looking at evolving a marketing organization from driving purely on intuition or mostly intuition to having the appropriate balance between being data driven and intuition driven, it's all about measurement and KPIs or business objectives. How to tie ultimately measurable key performance indicators rather than just, activity metrics have to sort of be confined across the very social channels. How many likes, shares, retweets, more important for example for folks to measure what is the quality of the fan base they've created or if you're looking at folks coming in through some other acquisition channel, don't just measure the volumetric aspects of these things, measure the business impact of them. And as CMOs start driving objectives down in the organization as we're seeing with many of our early alpha customers, their grassroots folks, the worker B level folks don't need to be particularly driven to start using data because in order to be measured based on data driven metrics, and then they start using these sort of tools as part of their day to day. Interesting, so it's a combination of kind of incentivizing and kind of setting the tone from the top but also kind of creating that organic movement to adopt these kind of technologies that approach us, interesting. Okay, oh for thanks for coming on theCUBE, always great to get your perspective. Now we could have probed you, probed you a little bit more about your startup but you know it's not our style to, we'll find out for the back channels, everything and we'll report it on silkenangle.com but not only kidding, we respect your stealth mode. Congratulations on your financing. You guys didn't know your big data, you have the big data chops there so congratulations and thanks much. Thanks for being on theCUBE. This is Silkenangle theCUBE, our exclusive coverage, this is theCUBE, our flagship program, we go out to the events, extract the signal from the noise. I'm John Furrier with Jeff Kelly, we'll be right back with our next guest. Got a great lineup of entrepreneurs, industry leaders, old school guys, I have Bud Colligan, Sir Karrish Swisher, he wanted to get her on as well. This is theCUBE at Stanford Excel Symposium, 17th year, we'll be right back with our next guest.