Thought Leaders in Big Data: Don DeLoach, CEO of Infobright (Part 5)

Sramana Mitra: Tell me a bit about your company. Is it a bootstrapped company or venture-funded? Very quickly, what is the history of the company?

Don DeLoach: We are privately-held and are venture-funded. As you would expect with venture-funded companies, there are limitations to what we will disclose but I can give you rough numbers. We have roughly 250 or so direct customers and about 30 OEM relationships that, by extension, have somewhere in the area of 250 to 300 large customers. We don’t count the smaller ones. One of our OEM relationships probably has 8,000 customers but we’re not including that in the number. The other thing is, by virtue of the 250 that we have, that covers among other things almost every telco operator in the world. >>>

Thought Leaders in Big Data: Don DeLoach, CEO of Infobright (Part 4)

Don DeLoach: I’ll just give you a quick example. One of our really good customers is JDS Uniphase. They had an application that did network troubleshooting but it was based on a traditional database. What they found was that the increase on the load of the network required their customers to continually index the database, which slowed down load speeds. In order to operate the overall application, it required more human capital and more hardware, and it was getting costly for both JDSU and their customers. >>>

Thought Leaders in Big Data: Don DeLoach, CEO of Infobright (Part 3)

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.

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Thought Leaders in Big Data: Don DeLoach, CEO of Infobright (Part 2)

Sramana Mitra: You’re telling me that your go-to-market strategy is OEM?

Don DeLoach: I would say it’s more and more OEM for sure. It had been a combination of direct sales mostly into the ad tech space and OEM sales into the people servicing mobile network operators. Without doubt, that’s over half of our business right now. It is asymptotically approaching 100%. The interesting thing there is that the mobile network operators are really at the forefront of driving the Internet of Things. When you step back and read the various industry analysis or some of the reports coming out of Cisco, they project 50 billion connected devices out there by 2020.

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Thought Leaders in Big Data: Don DeLoach, CEO of Infobright (Part 1)

Everyone in Big Data is anticipating the Internet of Things trend. Don discusses it as well, along with other issues.

Sramana Mitra: Don, let’s start with introducing our audience to you as well as Infobright.

Don DeLoach: I’m the President and CEO of Infobright. Infobright is a company that offers a purpose-built platform for storing and analyzing machine-generated data. If you had to classify us, it would be the column-based analytic database. There’s a number of clever offerings that I have a high respect for. We are not of the belief that there’s one silver bullet and nobody else does anything good. >>>

Building a Fat Startup: Apptio CEO Sunny Gupta (Part 1)

If you haven’t already, please study our Bootstrapping Course and Investor Introductions page.

One more look at what it takes to build a fat startup. Sunny Gupta discusses Apptio. The company raised a $7 million series A to get started, and then went to raise over $130 million thus far.

Sramana Mitra: Sunny, let’s start at the very beginning. Tell us a bit about yourself. Where were you born and raised? What kind of circumstances? Give us a backstory of the Apptio story.

Sunny Gupta: I was born in India in a town north of New Delhi. I went to school there and lived in New Delhi till I was 19. My father was in the government services. This is in the late ’80s and I didn’t feel, from a career perspective, that there were that many career options. >>>

Thought Leaders in Cloud Computing: Bob Renner, CEO of Liaison Technologies (Part 4)

Sramana Mitra: You’re saying that the structured data integration problem at scale is still an open problem? I’m looking for problems that are unsolved out there, not problems that you’ve solved already.

Bob Renner: I think scale is an unsolved problem at this point. A very difficult problem that you have to solve elegantly is cross-domain master data management (MDM). I think you’ve got a few products and solutions and implementation within a given domain and a less complex domain had been adequately handled, but I think cross-domain master data management is a problem that continues to be very use-case specific, and generally, there have not been a lot of solutions.

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Thought Leaders in Cloud Computing: Bob Renner, CEO of Liaison Technologies (Part 3)

Bob Renner: In one example and use case, we married our sales data with Twitter feeds so that we can access the API’s. We pull the data in, normalize and correlate it, and we created a dashboard that allowed our clients to look at sentiment. We were able to dimension that along with the sales data. We product a very different, simultaneous view of geography, volume of sales, and demographics of who they’re selling to and then generalize sentiment about how people are talking about the data from social media standpoint. The interesting part of that is it uses some forms of natural language processing and parsing to take free-form data and turn it into structured data from the Twitter feeds and marry it up to other structured data.

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