This paper explores the role of occupant-related data in supporting the digitalization of the built environment, focusing on how this data can enhance building performance, design, and operation. While research on smart technologies, fault detection, and building management systems has expanded, the integration of occupant behaviour data remains underexplored. Occupants significantly influence building energy performance and indoor environmental quality, yet quantifying and incorporating their behaviour into building systems has been a challenge. This study proposes a framework for incorporating occupant-related data into digital solutions. The paper examines three key applications of digital twins: performance verification, design optimization, and operational optimization. It highlights the need for diverse data collection methods, emphasizing that occupant behaviour data alone is insufficient without qualitative and contextual data. For performance verification, understanding occupant behaviour allows practitioners to distinguish between system faults, design flaws, and operational issues. For design optimization, focusing on occupant needs and preferences enables building designs that better meet the expectations of future users. For operational optimization, continuous data collection paired with machine learning techniques can personalize building control systems, enhancing comfort while improving performance. This research contributes to the development of guidelines for selecting and implementing data collection approaches to support digitalization in the built environment, with implications for building designers, operators, and occupants alike.