Data-Driven Decision-Making

Abstract

AbstractAddressing barriers to inclusion requires understanding the nature of the problem at the institutional level. Data collection and assessment are both crucial for this aim. In this chapter, we describe two important classes of data: (1) data on diversity that define the potential nature of the issues at stake and the need for change, and (2) data on assessing the usefulness of new programs, processes, and policies in creating a more diverse institution. Both sets of data are important for effective decision-making. At the same time, data analyses can be challenging because issues of equity and inclusion are complex and determining the basis of comparison or the “ideal” diversity target can be difficult. Nevertheless, data gathering and analysis are critical to assess progress and to provide a basis for both accountability and efficacy. Moreover, the ability to document that a problem indeed exists will help justify the need for change and, ideally, spur corrective action.