Cities face the challenge of transforming fragmented data into actionable insights for decision-making using Local Digital Twins (LDTs). The core issue is not merely accumulating data but optimizing the fl ow of information to be effi cient, secure, and aligned with governance for real-time applications like traffi c optimization and emergency alerts. To address this, Local Digital Twins must incorporate effi cient data fl ows using a fi ve-layer architecture framework. This ensures purposeful, secure, resilient, and responsive fl ows, enabling effective decision-making. Minimizing interoperability barriers and fostering continuous feedback loops transitions digital models into full-fl edged, bi-directional digital twins. This learning unit explores designing data fl ows using real-world case studies, emphasizing the importance of each fl ow layer— from sensors to application interfaces. By applying the Triple-Loop Learning model and Minimal Interoperability Mechanisms, participants map and critique data fl ows for better local gover- nance and transformative public value realization.