Data exhibiting heavy-tails in one or more dimensions is often studied using the paradigm of regular variation. In a multivariate setting this often leads to observing specific forms of dependence in the data, classically observed as asymptotic independence. But we may observe data concentration along particular directions which does not cover the full space also. This is observed in various data sets in finance, insurance, network trac, etc. We discuss the notions of asymptotic full dependence and asymptotic strong dependence for bivariate data along with the idea of hidden regular variation in these cases. Our analyses is exhibited on real data sets.