Thought Leaders in Big Data: Interview with Ron Bodkin, CEO of Think Big Analytics (Part 5)

SM: That is the question I was asking you. Are you working with the application layer companies, which are the AgilOnes, the Oversights and another 10 or 20 companies that are leaders in their categories? There is certainly as services business around that.

RB: What I said is that I believe there is a range of high value applications, where these composite apps can be put together using diverse data sets. We see there is a meaningful shift in data platforms. When there was a shift to relational, it meant that a lot of the pre-existing packaged applications needed to be reconceived. Likewise the shift to cloud necessitated a reconception of what an application was in the software as a service model. The same way we believe that the evolution of applications that take advantage of big data as such that there is clearly a range of companies that have been able to create value around big data. But we are also seeing customers who want to build more differentiated capabilities, integrating proprietary datasets of their own and taking advantage of the flexibility architecture. Our focus has been on those high value applications. We certainly bring patterns and skills, and we have partnered with domain experts in areas where there is a need for deeper domain expertise. We see a lot of value to be created there and I think the shift in technology in nature has created some interesting opportunities to build higher value custom applications.

SM: If I was running your business, I would map out the application leaders in each of those categories in each vertical that I am going in to and work with them, rather than reinvent that wheel. Of course there are lots of service opportunities around them and I am sure they are very open to partnering with services companies like yours to take that further.

RB: I appreciate the feedback. We constantly monitor the different companies out there for partnership opportunities.

SM: Where do you see gaps, where vertical applications do not exist, but are interesting areas where there is a lot of demand for big data solutions?

RB: The gaps tend to be the kinds of application that integrate diverse datasets, where there is a more standardized model. There is more opportunity by integrating multiple different datasets. To make it more concrete: It is easy to build a product that would leverage standard data from a double click or an atlas or a feed from Google Ad Exchange. But if you wanted to integrate in details of the CRM systems of the customer, the social data they have access to or geographic feeds, you start seeing the opportunity to build models that feed in multiple data sets that are not easily standardized and can’t be generalized in a simple way.

SM: Can you take us through some use cases of scenarios where you see that particular problem?

RB: One of the areas where we do a lot of work is machine data. We work with customers who have a desire to have raw information coming back from hardware or software that is installed in many locations. This concerns logs, configuration information and capacity usage integrated with transactions, licensing information, geographic data, etc. Being able to blend those datasets together and provide analytic insights into how the business is going, to be able to drive better product management off of data, improve customer service proactive models to predict failures of devices as well as being able to drive sales opportunities.

This segment is part 5 in the series : Thought Leaders in Big Data: Interview with Ron Bodkin, CEO of Think Big Analytics
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