This talk reviews the extension of the recent research which demonstrates that randomization offers a powerful tool for performing low-rank matrix approximation,such as the truncated singular value decomposition (SVD). These techniques exploit modern computational architectures more fully than classical methods and open the possibility of dealing with truly massive data sets. These methods use random sampling to identify a subspace that captures most of the action of a matrix.