The power prior provides a dynamic approach to translating data information to distributional information about the model parameters. Since its introduction, the power prior is playing an increasingly prominent role in many disciplines. Yet, a persistent question remains: how to best interpret the specified power weight value a0? This talk addresses the interpretation issue and links the value of the weight to the amount of information contained in the historical data set. The procedure can be extended to variations of the power prior, such as the partial borrowing power prior and the power prior for multiple data sets. The talk will also discuss software advancement in power prior analyses, which often relies on clever programming solutions that are problem-specific and difficult to generalize. We illustrate new features in SAS' BGLIMM procedure that enable you to fit the power prior to many models (hierarchical generalized linear models, repeated measurement models, missing data problems, etc.) with the simplest setup. We also discuss model selection and choosing a0 in single and multiple historical data sets settings.