There have been substantial advances in the development of states' education data systems over the past 20 years, supported by large investments from the federal government. However, the availability of modern data systems has not translated into meaningful improvements in how consequential state policies, such as funding and accountability policies, use data to identify students in need of additional resources and supports. Today, as has been the case for decades, states ubiquitously rely on blunt categorical indicators associated with disadvantage to identify these students, such as free and reduced-price lunch enrollment (among others). 20th-century technology is still being used to identify at-risk students in 21st-century schools. This post discusses why risk measurement needs to be thought of differently and how Predicted Academic Performance (PAP) could improve identification of at-risk students.