Learning Forward's Data standard advocates using data from a variety of sources and types--including student, educator, and system data--to plan, assess, and evaluate professional learning. This presents several challenges, beginning with the emphasis on a variety of sources and types. The pressures of No Child Left Behind have focused American educators on academic student data from large-scale, high-stakes tests-usually connected to school improvement plans that must be developed where Adequate Yearly Progress is not met. A second area of challenge relates to what the author knew and articulated forcefully about his students: They don't all need the same thing, and they don't all learn the same way. This article describes other kinds and sources of data that contribute to sound planning of professional learning and ways to use those multiple data sources to customize professional learning.