Data fl ows in Local Digital Twins must be designed to transition from static models to dynamic entities refl ecting real-time changes in urban environments. They should enable systems to update accurately and respond effectively, reducing the gap between the physical and digital worlds and ensuring governance is based on current and actionable data. To address the challenge, the learning unit presents data fl ows as structured pathways for seamless data movement. By understanding key types like real-time, batch, and event-based fl ows, students learn to design these systems to support real-time decision-making and adaptability. The unit emphasizes the importance of calibration and diversifi ed data formats. This learning unit covers the fundamentals of data fl ows in digital twins—highlighting timing, directionalities, and formats crucial for their design. Through the unit, learners explore real-world cases like COMPAIR and DUET to understand data lifecycle stages and how accurate calibration ensures reliable insight, enabling evidence-based governance and effective urban management.