Sets of sequence data used in phylogenetic analysis are often plagued by both random noise and systematic biases. Since the commonly used methods of phylogenetic reconstruction are designed to produce trees it is an important task to evaluate these trees a posteriori. Preferably, however, one would like to assess the suitability of the input data for phylogenetic analysis a priori and, if possible, obtain information on how to prune the data sets to improve the quality of phylogenetic reconstruction without introducing unwarranted biases. In the last few years several different approaches, algorithms, and software tools have been proposed for this purpose. Here we provide an overview of the state of the art and briefly discuss the most pressing open problems.