The basic approach to empirical modeling is pretty labor intensive and ad hoc. It is feasible for small to medium datasets but can't scale to massive datasets

The basic approach to empirical modeling is pretty labor intensive and ad hoc. It is feasible for small to medium datasets but can't scale to massive datasets -- in particular where there are lots of potential covariates. For those datasets to be analyzed, the modeling process needs a lot more automation. So I think[1] that's what big-data research is trying to solve most of all, and that seems to be what various companies are trying to sell. Obviously, none of this stuff is useful if it finds meaningless relationships; that's always true when people are looking at empirical data and is not unique to big-data. There is a lot of research on dealing with that exact issue in this setting. An old paper that looks at this stuff from an economics/finance perspective is here: http://weber.ucsd.edu/~hwhite/pub_files/hwcv-077.pdf http://weber.ucsd.edu/~hwhite/pub_files/hwcv-077.pdf and there's been a lot of research since then. I suspect that the popularity and faddish nature of "big data" right now comes from hope that those automation procedures will make it conceptually easier to do data analysis, but I don't really know if it will (and I have some doubts). [1] I am not a big-data person, so anyone more knowledgeable should jump in and correct me.