The purpose of this mixed methods phenomenological study was to explore the data-driven instructional decisions that elementary teachers make in literacy. Educators have moved towards a culture of being data-driven, and have declared data use in schools to be significant to school improvement and accountability. Yet, as school districts make great strides in creating a culture of data-driven decision making — collecting, analyzing and interpreting data — little is known about how individual teachers make sense of data and how they use the data to inform instruction. To explore the data-driven instructional decisions made by classroom teachers using literacy assessment data, multiple measures including a web-based survey, stratified random sampling for structured interviews, and videotaping of grade level data team meetings were utilized to investigate areas that influence data-driven instructional decision making: teachers' experience, knowledge and beliefs about literacy and literacy assessments most useful to teachers. This study also explored how teachers determine interventions for a group of students and individual students, whether data-driven decision making differences exist between K-2 (primary) and 3-5 (upper) teachers, and types of data-driven decision-making models used when analyzing literacy data. The findings of this study demonstrated that teachers shared common beliefs about the role of data in teaching, placed more value in common formative assessments, and identified strategies for student intervention based on their perceptions of the data. While findings of this study also demonstrated the need for teachers to want to align curriculum, instruction, and assessments, findings also indicated that teachers still perceived standardized testing items as important. Furthermore, key finding demonstrated that teachers' knowledge of assessment and literacy do influence decision making, and that while data-driven differences do exist among K-2 (primary) and 3-5 (upper) teachers, teachers employed the use of data-driven decision making models or behaviors that included transforming data into actionable knowledge to improve student learning and instructional decision making. The findings from this study contributed to the literature on teachers' instructional decision making and data-driven decision making. Recommendations for future practice include supporting data use in schools by building teacher capacity in assessment and data analysis. The findings of this study will have implications for districts and schools using student assessment information to inform instruction in order to better serve students at every level.