This study explores Data-Driven Decision Making (DDDM) in K–12 U.S. public schools. It emphasizes the effectiveness of assessment tools, how data is used to influence interventions, and the obstacles to implementing such decisions. The research combines findings from case studies, empirical research, and organizational reports through a qualitative literature review. Thematic analysis shows that when formative and diagnostic assessments are supported by simple technology, it provides timely insights that guide interventions like personalized learning and curriculum changes. However, broad implementation faces challenges such as limited teacher training, data overload, and resistance to change. The study underscores the importance of professional development, simplified data systems, and strong leadership in enhancing DDDM’s impact. It offers recommendations for improving data practices and advocates for further research in rural and non-academic educational settings to promote equity and efficiency.