Educators employ different methods of teaching and learning to help their students succeed. One such method is personalized learning, which aims to tailor instruction to the needs, talents, and skills of each individual learner. Rapid advances in technology platforms and digital content over the last decade have enabled more widespread use of personalized learning. As a result, many schools and districts may be interested in expanding its use across various content areas and grade levels. Effective personalized learning requires the collection, understanding, and use of data across many variables. Schools and districts that are interested in implementing personalized learning need to be prepared to ensure the data access, data privacy, and time and training to use data that are necessary to making personalized learning scalable. This document is designed to assist education agencies as they consider whether and how to expand their use of personalized learning. It provides an overview of personalized learning and describes best practices used by education agencies to collect data for personalized learning; to use those data to meet personalized learning goals; and to support the relationships, resources, and systems needed for effective use of data in personalized learning. It also provides a particular focus on data considerations needed by different locations depending on their approach to personalized learning, and considers questions such as which data elements are appropriate at the district or state levels. Because personalized learning is still a developing prospect in many locations, the concepts and examples provided here are intended to help facilitate idea sharing and discussion.