Myers & Finnigan Supplement

This online-only content supplements the article "Using Data to Guide Difficult Conversations around Structural Racism" by Lesli Myers and Kara Finnigan in VUE 48. 

More about Data-Driven Decision-Making

Our conversations begin with data as a starting point because as Marsh and colleagues (2006) point out, data-driven decision making (DDDM) involves “teachers, principals, and administrators systematically collecting and analyzing various types of data, including input, process, outcomes, and satisfaction data, to guide a range of decisions to help improve the success of students and schools” (p. 1). 

DDDM is linked to broader research on organizational learning and continuous improvement and traces back to debates of the 1970s, 1980s, and 1990s about site-based decision making, strategic planning, and measurement (Marsh, Pane & Hamilton 2006). Based upon this prior work, several scholars have developed theory-driven frameworks for DDDM that suggests that decisions can be informed by a variety of types of data, and that this data must then be analyzed to yield information that is actionable and leads to specific decisions in response to the analysis (Gill, Borden & Hallgren 2014; Mandanich 2012; Mandanich, Honey & Light 2006). 

A key to this process is what Argyris and Schön (1996) refer to as “double-loop learning” which involves reflection and suspension of deeply held beliefs and consideration of alternative views and practices. Double loop learning, in essence, involves a questioning process of beliefs and practices to both confront assumptions and to generate new learning. DDDM has not explicitly focused on racial equity, but this area of work provides strong guidance as to a process to use to move forward. 


Argyris, C. and D. Schön. 1996. Organizational Learning II: Theory, Method, and Practice. Reading, MA: Addison Wesley.

Gill, B., B. C. Borden, and K. Hallgren. 2014. A Conceptual Framework for Data-Driven Decision Making (No. 8142). Princeton, NJ: Mathematica Policy Research.

Mandanich, E. B. 2012. "A Perfect Time for Data Use: Using Data-Driven Decision Making to Inform Practice," Educational Psychologist 47, no. 2:71-85.

Mandanich, E. B., M. Honey, M., and D. Light. 2006. "A Theoretical Framework for Data-Driven Decision Making." Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.

Marsh, J. A., J. F. Pane, and L. S. Hamilton. 2006. Making Sense of Data-Driven Decision Making in Education: Evidence from Recent RAND Research. Santa Monica, CA: RAND Corporation.