Education systems worldwide have encouraged data use initiatives with the aim of improving student learning by means of data-driven decision making (DDDM). The use of student assessment data in particular has been promoted to guide teaching practices and progress student learning. Despite this, the adoption of DDDM practices by Australian classroom teachers has been slow. A review of current DDDM models conducted for this investigation highlights that research tends to focus on the later stages of data use, such as decision-making skills and targeted instruction. However, the activities that precede these are not well understood. Targeting outcomes without understanding the context or procedural mechanisms that produce them yields little insight into how to support and enhance teachers’ data use practices. Investigating current organisational activities at a micro-level is imperative for change initiatives to gain momentum and adoption. Consequently, the aim of this study is to qualitatively examine two foundational activities that affect teachers’ use of student assessment data: assessment data collection (ADC) and assessment data analysis (ADA). The qualitative study employed grounded theory methods as articulated by Corbin and Strauss (2008) to map teachers’ existing ADC and ADA processes and the salient factors influencing these processes. Drawing on data from twenty-three semi-structured interviews with experienced teachers from New South Wales primary schools, twenty-one salient processes and sixteen factors were identified as affecting teachers’ use of student assessment data to guide instruction. The salient process steps were then constructed into a unified flow diagram to identify inefficiencies and discrepancies in teacher data processes. Meanwhile, the factors were mapped to a business change model to formalise and examine the obstacles that are preventing the adoption of DDDM recommendations in Australian classrooms. This study’s teacher-oriented insight into current practices provides a foundation that guides fit-for-purpose change initiatives to augment and foster data use in the classroom.