 Winston Edmondson here at the Hadoop Summit. I've got the CEO and founder of Think Big Analytics, Ron Bodkin here. I want to find out a little bit about what his company does. Ron, thanks for being with me. Winston, it's a pleasure to be here. So tell me a little bit about your company, how and when you founded it and just kind of the goal that you have in mind. Sure, so Think Big Analytics were a purpose built services firm to help organizations really capitalize on big data, getting the measurable value quickly. We started the company three years ago and I had previously been the VP of engineering of a pioneer in the space called Quantcast. That's an online advertising and audience measurement company. We started using Hadoop in 2006 in production and Quantcast now has 50 petabytes of data and scores millions of events a second with predictive analytics models. So in leading the teams that built those analytic applications and data science efforts, I saw a huge opportunity to apply this to the enterprise and we decided to get the band back together. So I got two other senior folks who I'd worked with at a previous company. I was the founding CTO of called Seabridge and some of our senior people and we ramped up a team of folks to be experts in data science and big data applications. And so doing that, we've now done work for a number of leading technology, online, financial companies as well as other leaders in industries like retail and life sciences. I like what you said, you got the band back together to me it sounds like you have a culture or a community of just fun where you're doing what you're passionate about. Now one of the things that you talked about is the way enterprises is kind of hoarding data. A lot of companies are just compiling data and it's almost like they've got, in fact I read something that you said, data obesity. So tell me about how you guys are helping them get fit and kind of get rid of the data obesity. Sure, well data that's not being used it could be looked at as obesity, whereas when you're using data you're building it into muscle. Muscle weighs more but it does good things for you. So what we see is a lot of organizations are stuck in stage zero of big data adoption. They're still doing traditional samples, data warehouse BI and a lot of organizations that are moving into Hadoop and big data are doing the first stage which is starting to collect the data and scale it, a place to organize and process it. But if you don't have a business case, if you don't start to drive real impact to the business you're not getting the potential you could. We're passionate about helping companies go into later stages of adoption. So we see after the initial scale and cost stage real opportunities to drive better results for agile insights to be able to work with data in a raw format and spend more time learning about the business and testing ideas and less time wrangling data to answer those questions and then moving on to automating decisions with data science so you can optimize your current business with things like recommendation engines or models to predict before devices fail in the field and ultimately business transformation where you start to find new products and services like benchmarking services for customers that you can sell and create value out of analytics to complement your existing offerings to the marketplace. Fantastic analogy, I love the fitness analogy. But tell me a little bit about the stages because like you said, a lot of customers start with compiling this data but is there a time where it could be too soon to get into the analytics as far as maybe having data silos where it's not going to be beneficial to start? What would you recommend to a customer that's wondering when is the right time to get started? Well, it's a great question. And really we work with customers at all different stages of adoption because at each phase of adoption there's ways that you can start to build value in your organization, plan ahead for how you're going to really leverage this technology and it's really good to have that test and learn approach not only on your projects but in your organization to build up a center of excellence where you have first projects that are becoming more advanced, learning more interesting things, starting to do more sophisticated things and then broadening the pool out as the organization more generally can start to do agile analytics and leverage big data platforms. So organizations that are just getting started will often help them with a roadmap to plan out what are the pieces of an architecture and what are the first use cases that can just start to connect with real value which will involve some amount of data. Usually you get a lot more value out of integrating some less structured, more complex, high volume velocity data set with more traditional relational structured data. That could be taking your CRM data and your web logs or that could be taking data that's being phoned home from devices for support with transactions about how those devices are set up and configured. But either way, we see that a first project of taking some amount of data and then driving into proving value for a well-defined use case for the business, whether it's insights or automating decisions is often a great first pilot project. Seems like your company's in an enviable position and that a lot of services offered start very exciting. It's like, yeah, we can help you extrapolate and house this data. You're saying we can actually help you develop business models and analyze what you really have here. So tell me about some exciting use case scenarios that you've seen. Sure, you know, I couldn't agree more that we're at this inflection point where IT is becoming strategic, that innovating is important, using data to create value in the business is important. So when we started the company, we knew about advertising use cases and we knew about online recommendations and those continue to be really important, right? Being able to better look at customers and understand their behavior to interact with them effectively in the systems of engagement. But what's really been compelling to us is how much there is a need around a couple of other areas. One is around the internet of things. So organizations that have device data and need to be able to start understanding how customers are really using the devices they're selling. That could be companies like Network Appliance that we've worked with. If there are storage devices and sold in data centers, that could be energy companies that have smart meters that could be companies we're working with, you know, with embedded processors, devices that connect home for data, for security purposes, right? So there's a range of these. The pattern though of taking device data and integrating with business data to do things like predicting failures, better understanding the customer, serving them better, being able to identify how better to operate and use the service equipment. All of those are really powerful patterns that we've seen repeatedly across industries. So that's one area we think is really exciting and a huge opportunity to have an impact ultimately on the economy, right? That we think that the use of data science and big data is really going to change productivity in the economy and drive a lot of value in companies. The other area just briefly to touch on is in the financial arena, taking systems that are dealing with trade data and stepping back. So we're not talking about applying big data for high frequency trading in the nanoseconds, but instead being able to do surveillance and more sophisticated models across ranges of activity, full order book, value at risk. So basically taking more complex analyses, they're being done with low latency during the trading day, but at a much larger scale with a lot more complexity and sophistication. That's another area where we've seen a lot of opportunity and we're seeing Wall Street really start to pivot around to these big data opportunities. Fascinating. What would you say to the data scientists that say that this type of analytics really isn't all that we're hyping it up to be, that say that as humans, we are putting our own emotions into the data that we see. There are a number of statisticians that say we're chasing the wrong information. What would you say to them? Well, I think there's certainly no magic bullet. People come with their own biases and agendas and the best thing we can do is to change the conversation to be more data-driven, to be more fact-based, to have more shared places to look at things. So big data helps with all of those things as opposed to a more traditional, highest-paid person's opinion, which is very subjective or islands of analysis where no one can come together and reconcile their different views. Having a shared place, a shared way of looking at information moves you away from people saying that they each see something different into reconciling and understanding what's really going on. So we think it helps, but no technology automates away basic human challenges. I mean, just because you have a big data system doesn't mean that Department A wants to share their data with Department B. That's work, that's change management, that's culture. That's the hard stuff that comes with making these things work. Now you've got some neat technology on the horizon. Motorola has introduced pills that you swallow and they communicate what's happening in your body. A lot of exciting things that have some big data and implications. What are some trends that you're most excited about, kind of the future of big data? Well, I think certainly the notion of accelerating sensors, accelerating smart devices with software on board, that is a given, right? That there's an increasing amount of smart connected devices in the world and our ability to build these big data systems that really capture that data and create value, automate decisions and drive useful results in the real world are tremendous. So in addition to those swallowing sensors, you look at things like sequencing genomes being used for oncology clinics treating cancer and that's going to create a tsunami of data that will require big data in every clinic, right? And likewise you look at what does it mean to drive manufacturing efficiencies in supply chain as well as service devices in the field and we're seeing repeatable use cases where it's going to make a big difference to how our economy functions, being able to have big data systems as well as of course getting into systems of engagement. Today you've got a lot smarter, the web and mobile devices are a lot smarter. They need to be because our interfaces are shrinking down and we have less real estate. We need to be more engaged on an ongoing basis in our connected world. So personal analytics is an extension of that. So I mean, the thing that's exciting is this is a foundation for so much innovation, so many good things that can happen. And it's all starting off of some commonality and having communities form around open source standards that will allow a lot of collaboration and allow things to move a lot faster. Good stuff. Now for folks that maybe want to learn more about your company and maybe explore some of the services that you offer. And then for the folks that just want to kind of stay on the cutting edge and maybe follow a blog or a tweet that you may do, what's the best way for people to follow you or get in touch with you? Sure, so definitely those interested in our services and those interested in working at Think Big because we think we offer a really unique career opportunity for people that are excited about big data and want to work for some leading companies with great colleagues. You can come to our website at www.thinkbiganalytics.com and definitely there's lots more information around how we work with customers, what the opportunities are from a career standpoint, some of the technologies, links to our blog and certainly we'd love to hear from you. Fantastic. Winston Edmonton, Studio B, signing out.