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Data warehouse is proclaimed as the latest decision support technology. As data warehouses require a significant amount of organizational resources to develop, more research have been devoted to identifying the critical success factors and the formulas for assured investment return from data warehouses. This study proposes a bi-directional development approach for data warehouses in public sectors. The primary rationale for the proposed approach is the fundamentally different organizational goals of public sector organizations from private sector organizations. Whereas the ultimate goal of private sector organizations is profit making, public sector organizations have a set of conflicting goals including different social and political objectives. The star schema as a dimensional data model for data warehouse is not totally suitable for data warehouses that demand the analyses of both quantitative and qualitative measures. Using the data warehouse in the College of Business Administration at the California State University, Sacramento as a case study, we illustrate how the QQ (Quantitative and Qualitative) data schema accommodates the need of capturing both quantitative and qualitative information. In addition, we show the bidirectional top-down/bottom-up initiative, the formal/informal information collection, and the enterprise data warehouse/subject data mart architecture for the data warehouse.