Don DeLoach: The way the metadata layer is established is tantamount to indexing everything. So the maneuverability over the data especially for things like ad hoc queries and investigative analytics is very strong. That’s all done without the benefit or requirement of a database administrator. This typically is a low hardware requirement, a very low human capital requirement, and a very easy-to-use platform in terms of the accessibility and the maneuverability across that data. This is useful when you’re an ad tech firm and you’re trying to put together a guaranteed marketing campaign where you’re going to bid on guaranteeing a delivery of a campaign that will have at least 125 million impressions. Sometimes, you’ve to do very complex segmentation of the data to understand if you can step up and bid on a campaign like that. In order to do that, you’ve to maneuver through all kinds of data that you may or may not have been able to plan for.
This is where a traditional database that relies on indexing of the primary dimensions of the data breaks down. If you don’t really know what questions you’re going to ask, you can’t index everything or your load speeds are going to be terrible. In our case, it lends itself to being able to attack in any direction and get nice performance against that. For ad tech space, you have a lot of people who are browsing and segmenting loads and loads of data for the purposes of bidding on campaigns, tracking the performance of campaigns, or investigating where performance went wrong and why, so that they can bake that into further actions in the ad tech firm. We have probably 100 to 125 customers in that space.
The only thing I would add is a lot of our customers in the ad tech space use us in conjunction with Hadoop – some of them with MongoDB. What they’re doing there is they’re making Hadoop the primary repository for all of the advertising related data whether it’s online advertising, mobile advertising, or video advertising. Then they’ll use various elements of Apache ecosystem like Hive to sweep the data and grab the information that they’re most interested in for their campaigns and push it into Infobright so that they can dice and slice that data in ways that would otherwise be problematic. We have many instances of that as well. That’s what we are doing in the ad tech space.
I think what would be more of interest, because it represents directionally what we’re doing more and more, is what we’re doing in the telco space. In the telco space, the primary place that we are situated in that market is around the operational support – OSS systems. That would be things like network monitoring and network troubleshooting. These corporate networks and especially service providers have these monstrous networks. You’re talking about Verizon, AT&T, China Mobile, and British Telcom. These networks are vast. The load on these networks is getting greater and greater because of the proliferation of mobile devices. The troubleshooting function can often be very challenging. There are a number of solutions in the market. If you go to Mobile World Congress, you’ll see all of the players in this market. These solutions rely on the ability to understand and analyze vast amounts of data coming off the network.