Predictive analytics in higher education is a hot-button topic among educators and administrators as institutions strive to better serve students by becoming more data-informed. In this paper, the authors describe how predictive analytics are used in higher education to identify students who need extra support, steer students in courses they will do well in, and provide digital tools that can customize the learning process for individual students. The paper also outlines the ethical concerns involved in using data to make predictions and its impact on underrepresented students. The paper also addresses the increasing challenges with keeping data private. Often data about students are shared within the institution and with outside vendors to create predictive models for early-alert and recommender systems. This sharing of data allows students and faculty to remain anonymous, but this may be harder to do with the large data sets that predictive modeling often requires. The following are appended: (1) Predictive Analytics in Other Social Services; and (2) Interview List.