The availability of a variety of medical and healthcare data from various sources coupled with readily available high speed computing resources has opened up new frontiers and opportunities for the assessment of drug safety and health outcomes data. In particular, these real world data are quickly falling into the realm of big data in terms of volume, veracity, velocity, and variety. Visualizing these data can be useful to various stakeholders. For example for clinicians, quick comprehension of a patient's past medical history is important in deciding the patient's treatment regimen. Similarly for various healthcare researchers, these data can be useful in helping to better understand chronic diseases, comorbidity patterns, treatment patterns, safety, disease progression and patient outcomes, etc, all of which provide important insights into patient treatment and management. These data require new visualization techniques for knowledge discovery in terms of the considerations above. The discussion will focus on the value and use of visual analytics in real world data sources and visualization tools. The discussion will also touch on Bayesian thought in this setting.