eBay’s revenue is largely dependent on big data; by using, sorting, and filtering massive amounts of data, eBay makes sure that their customers see information that is catered to their individual interests. I suppose when people deal with the concept of Big Data so much on a daily basis, they start to think of other ways to usefully implement Big Data concepts. Even questioning the productivity of their own servers displays some thinking outside the box on eBay’s part.
Which begs the question: if a savvy tech company like eBay is able to save millions of dollars by applying Big Data concepts to their own servers, how much could other companies that deal with large volumes of data save? This is the relatively unexplored potential use of Big Data; Lisa Arthur mentions in her Forbes article “The Surprising Way eBay Used Big Data Analytics to Save Millions” that eBay’s success demonstrates the “critical importance of tearing down corporate silos”. eBay’s initiative should be the start of a widespread scramble for companies to save money by using and improving their own infrastructure. What are other points in the process that people can gather data from? How many millions of dollars could it save? These questions are probably running through the heads of creative data analysts around the clock as they work to find new and innovative ways to put Big Data to work.