The main "point" is that, (1) we can do some things with 'small' or 'medium' data, (2) with 'big' data we can do more, (3) but due to the curse of dimensionality, the 1 over the square root of n problem, etc., even big data cannot be big enough to be, net, much different than small or medium data.
Your point about what is 'big' about 'big' data is not really many more observations but many more variables. Okay. Of course there are problems trolling for causality or even just reliable relationships; we may find something in our net that is not real. But, if from big data we select and work with just a few variables, then with just those variables we are back to small or medium data.
So, the shortest 'point' is that it is so far not so clear just what is new and good that really needs big data. So I asked, for the big data wrench, what nut does it turn that needs turning? I'm not saying that there is no such nut; instead, so far I'm not hearing what the nut is and not seeing it on my own. So, I'm asking the big data people, what are the problems that want big data to solve. This goes to the OP claim of the importance of analysis of big data. I agree that the analysis is crucial, but without more idea of just what the problems, nuts, are we still need more to get excited about the opportunity for valuable analysis.
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:
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.
Your point about what is 'big' about 'big' data is not really many more observations but many more variables. Okay. Of course there are problems trolling for causality or even just reliable relationships; we may find something in our net that is not real. But, if from big data we select and work with just a few variables, then with just those variables we are back to small or medium data.
So, the shortest 'point' is that it is so far not so clear just what is new and good that really needs big data. So I asked, for the big data wrench, what nut does it turn that needs turning? I'm not saying that there is no such nut; instead, so far I'm not hearing what the nut is and not seeing it on my own. So, I'm asking the big data people, what are the problems that want big data to solve. This goes to the OP claim of the importance of analysis of big data. I agree that the analysis is crucial, but without more idea of just what the problems, nuts, are we still need more to get excited about the opportunity for valuable analysis.