For the more machine learning-minded folks, this happens almost always in doing inference with Exact Gaussian Processes (GP), where because of the non-parametric nature of a GP model, the covariance matrix grows with the size of data points. The inference routine is cubic in the number of data points. Hence, sparsity in the posited covariance matrix is _extremely_ important for fast inference.