Public school educators need to know how to use data that support informed decision making in order to improve educational processes. Theoretical models provide scant explanations of how public school educators actually apply data-driven decisions leaving educators with many unanswered questions about the practice.

The researcher found that a blend of external and internal forces contributed to data-driven decision-making implementation at this study's site. Other findings involve the similarities or differences between the practitioners' actual data-driven decision making processes and data-driven decision-making processes presented in literature. A major factor that influenced instructional decisions in order to address various students' learning needs came from computer-generated data that the practitioners reviewed.

Inquiry into technology-assisted data analysis and decision-making may provide a clearer understanding between data-driven decision-making and school improvement plans. Also, investigating how educational practitioners completed data-driven decision-making processes as compared to literature-based data-driven decision-making processes may provide additional information to school practitioners in order to assist them in making informed school improvement decisions at a more competent level.

The researcher collected and examined descriptions of the data-driven decision-making process from six elementary educators. The data were examined to three data-driven decision-making categories found in literature: (1) purpose of data-driven decisions, (2) processes of data-driven decision-making, and (3) contextual characteristics of data-driven decision-making.