I'll be presenting a keynote talk at the upcoming NASSCOM Big Data & Analytics Summit on Friday, June 27, 2014. In earlier talks, I have been emphasizing and introducing the advantages of predictive analytics. In this talk, I start from predictive analytics and move on to causal explanation. Synopsis: Big data have brought predictive analytics to the forefront by enabling organizations to generate micro-level predictions. Predictive analytic methods extract correlations and associations from rich datasets for the purpose of generating predictions. Personalized recommendations, offers, treatments, and interventions are examples of predictive analytics used in many data-rich-and-savvy organizations. While predictive analytics offer significant actionable value to companies by answering "who, what, when, where?", they are not capable of providing causal explanations for answering "why?" The good news is that statistical methods exist for causal investigation. The gold standard is randomized experiments, with alternative methods for cases when experiments are impossible. In the realm of Big Data, implementing such methods can offer new macro-level insights that can further strengthen data-driven decision making.