In this era of “Big Data” there are many sources of possible data that could be leveraged in the premarket regulatory environment. The effort to convert such data to evidence and then utilize such evidence in an efficient manner can be a challenge but the payoff can be enormous in terms of savings of time and resources. There are however inherent biases (especially selection biases) of observational data that need to be addressed as well as the confounders to be adjusted for due to different population characteristics. Also at issue is the generalizability of the studies. The potential uses of such real world evidence are: expansion of an indication, data for a control using propensity score methodology in a prospective manner, an empirical prior of a Bayesian study, a registry based randomized trial and the setting of Objective Performance Criteria (OPC) or a Performance Goal (PG).