 Okay, we're back here, wrapping up the Stratoconference. We're winding down day three. I'm John Furrier, the founder, so thank you for my co-host. I'm Dave Vellante of wikibon.org, and we're here with Ron Botkin, who's the CEO and founder of Think Big Analytics, very interesting company, doing a lot in the area of helping customers realize value out of big data. Ron, welcome back to theCUBE, good to see you again. Thanks Dave, nice to be here. John, nice to see you again. We love talking tech with folks who are one, building companies, building value. You're one of those folks out there, you've been around the block. I had a great chat last night talking about, you know, the beginning of open source, business value, and one of the things that's interesting with this show is, the top news of the event is, you know, things like security is a big conversation. Intel, EMC, the big money players are making big commercial moves in this business. Top-down's kind of coming together at the bottom up. That means there's acceptance, there's acceleration, and ultimately, there's demand. So you guys are doing very, very well on that demand, so give us an update on what's going on with Think Big Analyst because they're across the chasm, some have crossed the chasm and more are crossing, and you guys are helping people on the way. So tell us about what's up with you guys at Think Big. Sure, John, I'd love to. So we recently announced raising a seed round, $3 million to accelerate our growth. It's absolutely a year we're seeing big data moving quickly in the enterprise that organizations are realizing how much value can be created, and our strategy at Think Big is we consult with customers providing data science and engineering services to assemble analytic applications. So, you know, the conversation around security and around, you know, the increased interest in investment by not only startups, but the largest players in Hadoop and big data technology is because there's so much value that can be created as companies go, we kind of look at it as three stages of investment. You know, a lot of people started with big data in terms of having a business case for scalability and cost containment, then moving on into agile analytics and finally business optimization. And we think a lot of organizations are really moving into phase two where they're putting together business cases around creating new value, competitive advantage, and driving the business to create value faster. Do customers come to you with a notion of what they want to do, or do they come to you with the question of how do I get value out of this? Or is it both? You know, it's some of both. We have our Imagine services where we work with customers to co-create the ideas, put together what are the real valuable ways that you can get started, that you can build on where you're at, both from a standpoint of what are the business cases, what are the different use cases you can go after and create value, as well as understanding the technical roadmap, how you can sequence a rollout of technology and organizational development in order to achieve it. So those Imagine offerings often lead right into an organization then saying, well, let's start with our first pilot and build something that we can roll out. And the latter, the technical services are like, I'm presuming you've got a classic plan design, implement, you know, manage type of approach applied to big data Hadoop. Is that, first of all, is that correct? Or is it different than the sort of PDIM? Well, you know, we definitely have a very agile focus, right? So we believe that big data is very much around test and learn. So create something, get some feedback, adjust it, make it work better. So we believe in quick cycles of rollout. We have an onshore team that's our exclusive focus, working with our customers to align with the business. So we're agile, we're test and learn. We work with the business and the technology teams to go quickly. So within those cycles, there's definitely an element of, of course, you want to understand requirements in design, but you want to move in short cycles, not big waterfalls. Yeah, so you started there. That's, I mean, a lot of companies in, a lot of services companies are getting there. You've always been there. Okay, so that's different. Yeah, and we built the methodology for big data because that's all we do, it's all we've ever done. Now, for the Imagine services, talk about, because that requires a completely different methodology, right? You're bringing in different parts of the organization. You've got different types of, it's a bit more of a creative cycle going on. Do you describe the methodology around Imagine? Sure, so the Imagine sessions really pull together cross-functional teams. You have business executives, functional participants, technical leaders, our experts from our team coming together to talk through the business opportunities, do a brainstorm session where we identify them, talk through what the impact is, look at best practices, ideas from other industries, other companies, and how they might be realized in order to come up with functional priorities, as well as a deep dive into the infrastructure, reports and analytics, data applications, and organizational structure so that we can not only take a snapshot of current state and make recommendations, but guide a sequencing of build out of capacity in these applications to support the business objectives. What does the customer have to bring to the table, to those sessions, meetings, interactions? Is there brains or? Well, the customer has to bring kind of participation from the right members of the team, right? It's really important to have both the business side and the technology side engaged, right? To speak to the priorities, to speak to what's really going to resonate, what's going to drive value in the organization, what's feasible and what can be achieved, as well as on the technical side understanding, big data, Kadoop, NoSQL streaming real time, distributed data science, these are really valuable, but they don't stand in a vacuum. Companies are complimenting, extending their current capabilities with these exciting new things. So understanding what they are, understanding the data sets, understanding the trajectory of the business as it stands, so that you can lay in big data technologies and create new value is critical. And talk about being metrics driven. I would imagine your clients are saying, okay, well, this sounds good, but I need metrics, I need to understand the business case, I need to understand how we're going to measure success. I mean, they must really put that pressure on you, obviously, because they want to get an ROI on the investment. Talk about that a little bit. Yeah, absolutely. That's why we say we are interested in creating measurable value for big data. As people launch into these applications, they'll look at metrics. It could be different metrics for different applications. Device data, gathering support data from devices that are deployed out in the field. You might be looking at reducing the time to resolve problems. You might be looking at proactive service to fix broken, distrized before they fail. If you look into customer engagement scenarios to talk about lift in terms of higher response rate from a consumer or price optimization or reduced fraud or waste, right? So the metrics can vary depending on the application, but you're absolutely right that you have an objective in terms of creating measurable value, and it's certainly true that some of the metrics are around cost cutting, cost containment, scalability, but the more innovative applications are really driving top-line growth and new opportunities in the business and optimization of how the business operates. Telephone numbers compared to cutting cost. Go ahead, Jim. Talk about how many people you have in the company real quick and how long it has been around for. So we're just about 50 people now. We started the company back in 2010, so almost three years ago, have been growing quickly. You guys have a nice partnership list here. I mean, you have a lot of big players. How do you keep up with all this? Because in the growing market, it's like holding onto the rocket ship. You have things going crazy. You have all the different vendors, Amazon, Datameer, Talon, CloudEra, Hortonwork, Green Plum, AppBar, Revolution, DataStacks, Arrow Spikes on the list, all companies that we know. I think there's a lot of effort to kind of stay up to speed on this stuff, so how do you guys do that? I mean, besides working a zillion hours? Well, it's a great question. You know what I mean? I'd say that there's a couple of things that play in our favor. One is because we're totally focused on big data. It's all we do. It's not a side hobby. It's not a little practice we're adding on. It's all we do. We live and breathe this stuff. We're here, I'm here til midnight, meeting with people, catching up, talking about what's going on at Straton conferences like this. Our architects are networked. We're in the community. We have folks like Mike Siegel, who leads the Chicago Hadoop user group, who's out speaking and engaged. So we're really engaged in the community and working closely with our partners to achieve success with customers and always really open. We like you get overwhelmed by smart people that are starting companies with great ideas in the big data space. But we focus on those partners where there's measurable value, where there's traction, where we've really had great success using the technology. Where is some of the, I'm asking it's an educational market, training and skills that are needed and then implementations are always the key to these open source and all these new frameworks and partnerships. And the clients are crossing that bridge. So you do a lot of that work and is it more that the clients aren't as sophisticated in terms of churn and burn deployments? So you have that insight and you're kind of doing those kinds of things. Is that, do you see more educational in the front end or implementations? Well, you know, I'd say that a lot of the value proposition is we've had the experience of working across multiple use cases, multiple industries, multiple customers. We've worked with a lot of the technology and we understand how the pieces fit together, right? That big data isn't just about Hadoop and it's not just about NoSQL or just about streaming data but it's elegantly orchestrating these pieces together to create value, right? So, we're getting the hook here so we have to let you go but I want to give you a final word for the folks out there watching here at Stratoconference, obviously energizing intoxicating on one end but also very motivating and energizing on the other on the tactical side. Could you share with folks out there your impression of Stratoconference and then the market in general over the next 12 months of your vision? Absolutely, you know, it's very exciting being at Stratoconference. I'd say each year, Strata takes a big step forward in terms of the level of conversation, the sophistication of the offerings, the amount of commitment and the kind of results that can be shared. So, we're pretty excited about that. You know, we see increasing emphasis on big data from all the different players and increasing realization that it takes an integrated approach to create value. So, we're really bullish about how this year is going to shape up in the years beyond where value will be created by leveraging data to create measurable solutions. Think big analytics. Thanks for coming on. We appreciate it. We'll be right back with our next guest as we wind down day three of blanket coverage of Strata here in Silicon Valley. Right back.