I think defining "big data" as something that depends on the algorithm you are applying is not very useful. In that sense, almost anything is big data when you

I think defining "big data" as something that depends on the algorithm you are applying is not very useful. In that sense, almost anything is big data when you are trying to solve an NP-complete problem (nice course about algorithmic approaches to these problems: https://www.coursera.org/course/optimization https://www.coursera.org/course/optimization). The problem is, if you define "big data" as something that depends on the algorithm, then it makes no sense to include the word "data" in it. The expression "big data" as it is commonly used refers to flows of data so big that you need specialized approaches even when applying simple transformations to the data. Since the dawn of computing we've always wanted to solve problems, with small or large amounts of data, that required complex algorithms. The usage of a new expression is justified by the fact that huge flows of data are now available to many companies (mainly because of the web), not because these companies are attempting to perform extremely complicated transformations to the data. TL;DR: Big data means big volume/flow of data, and not (as you are defining it) using large Big-O complexity algorithms on some set of data. In fact, the size of big data precludes applying large Big-O complexity algorithms to said data.