Policy Support Systems (PSS) enable transforming data into actionable, evidence-based governance recommendations to aid policymakers. Aligning decisions with the 2030 Sustainable Development Agenda makes the implementation of PSS even more complex, as the Sustainable Development Goals (SDGs) encompass a broad and interconnected set of goals and targets. This necessitates careful consideration of multiple dimensions and the synergies and trade-offs that exist across them. Building on the vast availability of Open Government Data (OGD), we propose a novel data-driven PSS architecture that provides an end-to-end solution for policymakers by transforming raw data into actionable insights through prescriptive dashboards, which we term as do-boards. The proposed architecture integrates SDG-based contextual mapping, semantic data retrieval, correlation-based intervention modelling, and prescriptive analysis. Drawing on structured semantic data pipelines, the system generates targeted policy prescriptions mapped to relevant government schemes. The proposed architecture facilitates granular decision-making at the state, district, taluka, and village levels. The manuscript presents an execution workflow that utilizes do-boards to address the case study of student dropout rates in the Indian state of Karnataka, aligning with SDG 4: Quality Education. The proposed research highlights the potential of do-boards in bridging the gap between data analysis and actionable insights. This enables evidence-based policymaking and contributes to sustainable development.