As states, districts, and schools search for strategies to help raise student achievement and improve college readiness, they are using an increasingly wide range of data to inform decision at all levels of the education system. In this data-rich environment, education decision makers have access to a wealth of information about students, teachers, administrators, and other staff; organizational finances and operations; and the communities that educational institutions serve. These data, however, could be wasted or even misleading if decision makers do not understand the benefits and limitations of the data, the types of data relevant for the decisions they are confronted with, and how data can be appropriately used for decision making.

This paper will discuss a framework that provides a comprehensive picture of the DDDM process in education. The framework was developed for the Bill & Melinda Gates Foundation based upon knowledge generated from an evaluation conducted by Mathematica Policy Research of four strategic data use initiatives and from existing literature on data use in education.

Two figures encompass the framework. The first figure provides a high-level, generalized theory of action--a causal chain--for how DDDM can lead to improved student achievement, and the supports and incentives needed to make effective data use possible. The second figure maps the process of DDDM at different levels of the education system, from classroom to state superintendent’s office, depicting the types of decisions that might be informed by data, the types of data needed to inform different decisions, and the importance of determining that the data are both relevant and diagnostic.