This is what the book Social Media Mining calls the Big Data Paradox:
Social media data is undoubtedly big. However, when we zoom into individuals for whom, for example, we would like to make relevant recommendations, we often have little data for each specific individual. We have to exploit the characteristics of social media and use its multidimensional, multisource, and multisite data to aggregate information with sufficient statistics for effective mining.
Brad Efron said something similar:
… enormous data sets often consist of enormous numbers of small sets of data, none of which by themselves are enough to solve the thing you are interested in, and they fit together in some complicated way.
Big data doesn’t always tell us directly what we’d like to know. It may give us a gargantuan amount of slightly related data, from which we may be able to tease out what we want.
Related post: New data, not just bigger data