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Big Data Meets Travel and P2P: Rearden/Deem Mine For Insight to Improve Shopping and Buying

travel agent

Rearden is beginning to inject a new set of useful predictive analytics into the business travel area. Their approach incorporates the level of quantitative and correlation analysis of historical data (both personal and publicly available) . Among other dataset combinations/mash-ups layered with predictive analytics on top, Rearden is leveraging Bureau of Transportation statistics covering 67 million flights in the past decade and using this information as a foundation to deliver personalized travel analytics to assist with decisions at the point of booking and travel.

The platform is thus able to predict and recommend connecting schedule, flight dates and times, specific carrier and flight number, etc. The approach goes far beyond the “on-time” recommendations for single hops that Orbitz (Richard’s former employer) and other travel sites provide by taking into account the realities of actual travel conditions. It’s somewhat analogous to some of the work SAP has done in the supply risk area by applying predictive analytics based on more than just single-site (and company) supplier performance ratings with the InfoNet offering.

Yet Rearden is embedding this capability within their core offering rather than delivering it as a stand-alone service or solution.

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