Sramana Mitra: Does that mean they are giving comScore access to their Google Analytics?
Josh Rogers: No. They will download a piece of software that sits in the browser and that sends the information back to comScore. >>>
Sramana Mitra: From among the 2,000 customers you cater to, let’s do a few use cases of the kinds of business applications you are facilitating.
Josh Rogers: We see a few different scenarios where people are struggling. We have a number of customers that have made large investments in data warehousing environments. Perhaps they made investments in large-scale data integration platforms. >>>
Ron Bodkin is the chief executive officer of Think Big Analytics, a services firm that helps organizations implement big data applications and create value for big data. Ron holds a BS in math and computer science from McGill University and a masters in computer science from MIT. He founded several successful companies, one of which he led to a staff of 900 service consultants. In this interview, Ron talks about Think Big Analytics’ solution for its customers, who the company partners up with and what technologies it uses in order to create its offering. Furthermore, he provides entrepreneurs with insights into gaps in the industry.
Sramana Mitra: Ron, why don’t you tell us a little bit about what Think Big Analytics is all about? What do you do? What problems do you solve? Who are your customers?
Ron Bodkin: Think Big Analytics is a services firm that helps organizations create measurable value for big data. We work with our customers, and we provide services to help them implement big data applications with our engineering and data sciences services. >>>
Josh Rogers is the senior vice president of Data Integration Business at Syncsort. Josh holds an M.B.A. from Harvard Business School and a B.A. in economics from Davidson College. He had previously worked for Bank of America, Endeca, and IBM and has more than six years of experience in data management. In this interview he talks about Syncsort’s solutions to provide improved sort capabilities for big data analytics and gives insights into open problems in this space.
Sramana Mitra: Josh, let’s start with setting some context about Syncsort. What do you do as a company? Who are the customers? What is the primary value proposition? >>>
Sramana Mitra: Once the architecture of the world is much more mature, you are coming in at the heels of a platform as a service trend. So, you basically provide a platform as a service, provide the heuristic layer on top of that, etc. We were talking about business models earlier. You can charge a per user fee, for example. Then the vertical folks jump at their piece of it.
Robert Youngjohns: Maybe I am a little old fashioned – I have been in the industry for a long time. The way I would use this is as an operating system for human information. If you think about an operating system you abstract common functionalities, access to data, etc. which is why I formulated that term, because another important part of it is how we can actually see in to all the various data sources. >>>
Sramana Mitra: How are you going about trying to solve that problem? Are you going at it in a horizontal mode, or are you trying to create verticalized solutions? The interviews that we have been doing in our big data coverage have seen a ton of companies that are working on certain verticals. >>>
Sramana Mitra: I would like you to take off the HP hat and wear more of an industry thought leader hat. Give me some pointers to where you see open problems.
Robert Youngjohns: I think the issue of unstructured data is one of the big problems facing the industry. It is really hard science to do this well. >>>
Sramana Mitra: What is the state of the union on video analysis?
Robert Youngjohns: It is developing very quickly, and we have very powerful tools. It started way back with something as simple as number plate recognition, which is now very established. We have a demo app where we show people where we are taking pretty much every TV feed we can get from anywhere in the world and then do real-time analysis on what that feed is about. It turns out to be a complex problem, but not as complex as people may think. In terms of news media, people use a lot of subtitling. We can sense and detect those and then use that to categorize the information stream we are getting in. Then, by looking at the content of those streams, we can apply negative or positive sentiment to them. >>>