 Hi, I'm Jeff Frick. We are on the ground at Cassandra Summit 2014 in lovely San Francisco at the Weston St. Francis. We're out here at the conference, the fifth year of Cassandra Summit, getting a feel for what's going on, grabbing people out of the hallways and getting an update. So I'm joined next by Clint Kelly from Webe Data. Hi, Jeff. Great to stop by. Thanks for letting us grab you. My pleasure. I want to talk a little bit about, before we got started, Webe Data, give us a quick update on the company. Yeah, sure. So we build a personalization platform on top of big data technologies like Cassandra and Hadoop. So we help companies in vertical markets, such as retail and finance and healthcare, provide personalized experiences for their customers. So we talked a little bit earlier with another guest about kind of the media side of where Cassandra plays there for streaming video and streaming audio, etc. You're talking about a specialty in retail. So I wonder if you could share some stories about, you know, what are retailers doing with this capability? Sure. So retailers typically will capture all of the data they have about customers in a database like Cassandra. With the customers we work with, we use Webe Data software, for example. They're capturing all of the users' clicks every time they add something to a product or a wish list or view something. All of that gets captured as time series data in Cassandra. Then they use Webe Data software to run some analytics over this data and use those analytics along with some real-time information that they have about customers to serve them personalized content. So what's different? I mean, online retailing has been going on for a long time, right? We know recommendation engines. I mean, it is kind of scary that after I'm shopping for some item, as soon as I'm navigating someplace else, it pops up in my Google ad. So what are some of the things they could do now that they couldn't do? And is this exclusively for the online shopping experience or we hear talk about it moving, you know, kind of onto the floor with sensors and other things that now can see how I actually look at the shelf the same way as how I navigate around a web page? Yeah. So those are great questions. So first, this is different from normal product recommendations. It's a much more powerful, flexible engine. So we go beyond just recommending products, like when you add something to your shopping cart, for example, to do things like personalized offers. If you're using a mobile device and you come into a store, we can send you special offers, for example, or point you to certain products that are on sale or that are close to your location. Based on all this information that the retailers captured about you and some analytics mixed in with real-time contexts, like where you are being in a physical store, for example, or how you've interacted with a mobile application recently. So talk a bit about the growth of the data inputs and the amount of different types of inputs that you can now draw from to drive that engine that you couldn't before with mobile devices and other kind of things. Maybe we're not thinking about that or out there as good sources of data. Yeah, good point. That's one of the great things about working on top of a database like Cassandra is it's so flexible and it's so big that we can pull in data from the ways that users interact with the brand on all kinds of different platforms. So I described earlier that users, you know, they click on a website or they view products on a website. We capture all of that. That's pretty standard. But we can also do things like with some of the new technologies coming out recently, like iBeacon, for example, look at, keep track of what users are doing in physical stores. We could look at how users interact with brands through all kinds of different channels, even maybe wearables, for example, with Apple Watch coming out. That's right. And what about privacy? Right? As I hear these things, you know, obviously the little privacy flags go off. You know, I don't, I guess we know everything on the Internet. It's on the Internet and it's there for forever. But, you know, how are people dealing with kind of the privacy implications of really collecting this much data around particular individuals, you know, the market of one? Yes, good question. So one of the nice things about our solution at Webidata is that we offer an on-premise solution. So the retailers we're looking at are very conscious of security concerns. And so if they use our technology, they can build out this big data infrastructure all on-premise. So they don't have to ship their data off-site at all. Yeah. Well, it's scary. I mean, we just had the most useful one, right? The Home Depot, the Home Depot breach, which is all over the news. And it seems to happen pretty regularly. But hopefully you guys are working to keep that stuff on lock down. Yes, absolutely. All right. Well, thanks for stopping by. My pleasure. Again, I'm Jeff Rickward, Cassandra Summit 2014 in San Francisco at the lovely Western St. Francis.