<![CDATA[Healthcare organizations operate a fragmented digital landscape in which hospital information systems (HIS), electronic health records (EHR), laboratory systems, billing platforms, and departmental applications are optimized for transaction processing but not for integrated analysis. The resulting interoperability gaps, semantic inconsistency, duplicated records, and uneven data quality constrain enterprise reporting and limit higher-value analytics. This paper substantially proposes implementable enterprise data warehouse architecture, formalizing its data-quality and conformance mechanisms, and validating the design through experimental analytics use case. The proposed framework combines an integration layer for ETL/ELT, conformed dimensions, departmental marts, governance controls, and an analytics layer for OLAP and machine learning. To demonstrate practical value, the paper evaluates the framework on a de-identified inpatient diabetes dataset comprising 101,766 encounters and 50 raw attributes. The experimental pipeline performs profiling, conformance mapping, diagnosis grouping, missing-value treatment, and dimensional modeling before training benchmark readmission models. The best ranking performance is obtained by XGBoost with an AUROC of 0.688 and an AUPRC of 0.235, while threshold tuning improves recall-oriented operational utility. The results show that healthcare warehousing should not be framed merely as centralized storage; rather, it is an architectural mechanism for interoperability, data quality control, reproducible analytics, and decision support. The manuscript concludes with implementation guidance and limitations relevant to hospitals seeking a scalable, governance-aware warehousing program.]]>
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