Big data in conjunction with artificial intelligence and machine learning are revolutionizing the potential and risk of predictive business analytics. Firms have extensive records on customer and employee behavior. While on the one hand this allows firms to deliver an increasingly sophisticated product offering that is customized to individual needs and preferences, it also has the potential to extract private information that a customer may prefer not to share. Similarly, firms now have the potential to use algorithms to replace workers in certain domains. It is thus more important than ever to engage with the opportunities presented by these new technologies while also managing and regulating the risks associated with them. We will explore three examples. First, we will see how fine grained data on household energy consumption can help us design more successful energy efficiency programs. Then, we will investigate whether algorithms can replace bank managers. And, lastly, we will see how consumer credit data can predict mortality.The presentation will be followed by a discussion moderated by Martin Wagner who will share some of his experiences with big data in a central bank context. We want to discuss the implications of big data and business analytics for education, research, firms and organizations.