Strong (or quantitative) data processing inequalities provide sharp estimates of the rate at which a noisy channel “destroys” information. In this talk, I will present some recent results on strong data processing inequalities in discrete settings, with a focus on their use for quantifying the mixing behavior of Markov Chain Monte Carlo (MCMC) algorithms and the decay of correlations in graphical models.