I personally find the need to go back and forth quite a bit between different tools when working even on a single dataset. Say, i'll do some work with text part of the dataset in Python, then use Matlab for some basic quantitative analysis, then use R for some advanced statistics if I have to, port those results back to Matlab/Python for speed, then back to R to sweave, maybe some spreadsheet stuff too, etc... So for me, for every project I need to have my dataset in easily accessible format (csv/someSQL/etc) and I need scripts in every language that I use, that would communicate with the data source and get "up to speed" right away.
It's little tips like that I think people should be sharing more.
make sure you look at some older posts
...I'm baffled at the sarc and outrage every time something like this comes up.
Data analyst is a massively broad term that could be anyone from a junior ad-hoc SQL script writer to a guy who sits on a DWH generating leads using complex models and algorithms.
There is a need to distinguish these things from one another and the new definitions and tags are trying to suffice this need... what's your boggle?
New names are all good if they name new concepts. But new names for old things just sound like buzzwords.