  Data-collapse is a way of establishing scaling and extracting associated
exponents in problems showing self-similar or self-affine characteristics as
e.g. in equilibrium or non-equilibrium phase transitions, in critical phases,
in dynamics of complex systems and many others. We propose a measure to
quantify the nature of data collapse. Via a minimization of this measure, the
exponents and their error-bars can be obtained. The procedure is illustrated by
considering finite-size-scaling near phase transitions and quite strikingly
recovering the exact exponents.
