Academic analytics helps address the public's desire for institutional accountability with regard to student success, given the widespread concern over the cost of higher education and the difficult economic and budgetary conditions prevailing worldwide. Purdue University's Signals project applies the principles of analytics widely used in business intelligence circles to the problem of improving student success within a course and, hence, improving the institution's retention and graduation rates over time. Through its early stages, the Signals project's success has demonstrated the potential of academic analytics. Those early efforts have led to additional projects to develop: (1) Student success algorithms (SSAs) customized by course; (2) Intervention messages sent to students; and (3) New strategies for identifying students at risk. The premise behind Signals is fairly simple--utilize the data collected by instructional tools to determine in real time which students might be at risk, partially indicated by their effort within a course. Through analytics, the institution mines large data sets continually collected by these tools and applies statistical techniques to predict which students might be falling behind. The goal is to produce "actionable intelligence"--in this case, guiding students to appropriate help resources and explaining how to use them. Early reviews by administrators, faculty, and students have been positive, as has empirical data on the system's impact. The Signals system is based on a Purdue-developed SSA designed to provide students early warning--as early as the second week of the semester--of potential problems in a course by providing near real-time status updates of performance and effort in a course. Each update provides the student with detailed, positive steps to take in averting trouble. By no means is Purdue unique in its interest in academic analytics. Institutions across the world, large and small, public and private, research and teaching, have begun forays into various data source modeling strategies in an effort to find actionable data to support their goals. This article offers a snapshot of the experience at Purdue. (Contains 4 figures and 5 endnotes.)