I apologize in advance, I'm new to statistics.  I have a large (millions) dataset (the US Census American Community Survey) with 286 attributes.  I've calculated the mean, variance and standard deviation for each attribute, and I would like to order them by "variability" (roughly, that is - if the concept I'm driving at is not a statistical one, I'll settle for one that is just intuitively appealing.)  Obviously I can rank them by sigma, highest to lowest (ranging from very high to very low in absolute terms, on the order of thousands down to <1.0).  But is that meaningful?  Does a large sigma (or variance) mean it's more "variable", or should I be looking for the largest *RATIO* of sigma to mean?  I can't find any reference to a concept like that in my textbooks, but it seems to me to convey more meaning than a variance/sigma on its own.  (The point, if you haven't guessed, is to reduce the dimensionality to something more manageable by discarding the attributes with the least variability.)