Understanding the experience of students in a computing program, or even a single course within an academic department, is no easy feat. We present a system for collecting intersectional, longitudinal data to track the status and health of a computing program or individual course. While previous work has tracked enrollment, withdrawal, and failure rates, in most cases, the data is collected over a brief time span and is inspected in a one-dimensional manner–along only the axis of race or only the axis of gender. Our deployed system has been used to collect intersectional, longitudinal data for CS 1, 2, and 3 courses alongside program completion rates at 52 universities in the U.S. The collected data spans 2018 - 2022, with ongoing collection through the present. Universities submit their internally held outcome data term by term; data for each (de-identified) student includes race/ethnicity, gender identity, major, transfer status, and course outcome (pass, fail or withdraw). Drawing on our experience working with these universities we present guidelines for the analysis of intersectional, longitudinal data alongside our recommendations for actionable next steps. We present three case studies demonstrating how to help an institution understand their own computing program and develop interventions–specifically with an eye toward broadening participation in computing. This work is a direct answer to calls to collect and analyze data along many dimensions.