Theoretical differences between data-driven decision-making (DDDM), data-based decision-making (DBDM), and data-informed decision-making (DIDM) approaches have received relatively limited attention in digital pedagogical environments. This conceptual review draws on peer-reviewed literature between 2009 and 2025 to clarify these differences and examine their pedagogical implications. While DDDM is often reliant on systematic data use and predictive tools, DBDM is concerned with collaborative interpretation supported by digital dashboards and formalized methodologies, and DIDM integrates professional judgment with digital evidence and ethical considerations. The analysis highlights the impact of digital environments – such as learning management systems, digital trace data, and new AI-supported tools – on educational decision-making. Each approach has distinct benefits and challenges that have implications for teacher professional development and learning as well as institutional practices. An understanding of these differences is essential to effectively balance technological capabilities with pedagogical expertise, as well as to foster ethical and context-aware data use in contemporary education.