Changes in biomedical science, public policy, information technology, and electronic heath record (EHR) adoption have converged recently to enable a transformation in the delivery, efficiency, and effectiveness of health care. While analyzing structured electronic records have proven useful in many different contexts, the true richness and complexity of health records—roughly 80 percent—lies within the clinical notes, which are free-text reports written by doctors and nurses in their daily practice. With the vast computing infrastructure and sophisticated ontologies, natural language processing, data-mining and machine learning tools available to us today, we are poised to cross the “threshold of sufficient data” and begin the practice of data-driven medicine. With the availability of tools for automated annotation and the existence of over 250 biomedical ontologies, over 1500 public data repositories and increasing access to electronic medical data, it’s time for Big Data mining in medicine. I will illustrate the kinds of analyses that are possible when we embrace complexity and make use of the best ally we have: the unreasonable effectiveness of data.