Triangulate Before you conclude
One data point is a signal. Two corroborating data points start to be evidence. If you find an unexpected result, check it against a second source or a different time window before building a narrative around it.
Document your pull as you go
Note the filters you applied, the cohort window you used, and the date you ran the analysis. This is especially important for accreditation work, where you may need to reproduce or explain your methodology months later.
Start broad, then narrow
Run a high-level look at a college, school, or program before drilling into subgroups. Patterns visible at the aggregate level will tell you where to focus your deeper exploration.
Segment by graduation cohort, not enrollment year
Outcomes analysis is more meaningful when tied to when students completed their degree, not when they started. This gives you a cleaner window for measuring post-graduation outcomes.
Share your findings with context
Raw numbers without context can mislead. When you share data, include the cohort definition, the time window, and any significant caveats. This builds credibility and prevents misinterpretation downstream.
Not sure where to start?
If you have a use case in mind but are not sure which module handles it, or if you want a walkthrough of the data for your specific institution, reach out to your Partner Success Associate. We are happy to run a working session tailored to your goals.