Data monitoring is an essential component of clinical trial enterprise to ensure scientific integrity, data quality, and safety of study participants. As clinical trials have become increasingly complex logistically and geographically, monitoring approaches should evolve accordingly. In response to this challenge, the FDA released its final guidance for industry for oversight of clinical investigations in August 2013. In the guidance, the FDA clarifies that the traditional monitoring model of frequent on-site monitoring visit with 100% source data verification is not the Agency’s expectation but only a misperception. In fact, the FDA encourages the industry to think outside of the traditional model and consider alternative risk-based monitoring approaches. In contrast to the traditional model of 100% source data verification, the risk-based approach “focuses on critical study parameters and relies on a combination of monitoring activities to oversee a study effectively". By leveraging modern information technologies and statistical analysis of trial data, such as historical pattern, aggregated statistics, and site-level performance metrics, an adequately implemented risk-based approach can uncover some data irregularities that may not be detectable by the traditional model. In this presentation, I will describe some risk-based monitoring approaches. Next, I will highlight some important considerations for sponsors when planning and implementing risk-based monitoring. Finally, I will discuss various crucial roles that statisticians play in the paradigm of risk-based monitoring and its evolution.