<![CDATA[Diabetes Mellitus is a chronic disease with an increasing global prevalence, requiring integrated and data-driven health data management. However, healthcare institutions often face challenges in managing patient data that are distributed across multiple formats and fragmented storage systems (data silos), which hinders strategic analysis and effective decision-making. This study aims to design and implement a Data Warehouse to integrate diabetes patient data as a foundation for clinical and managerial decision support. The research methodology applies a multidimensional schema design using the Star Schema approach, consisting of one fact table and three main dimension tables: demographics, lifestyle, and medical conditions. The Extract, Transform, and Load (ETL) process was implemented using SQL Server Integration Services (SSIS) to cleanse and centralize data from operational sources. The dataset used in this study consists of 10,000 anonymized patient records that have undergone data profiling and data cleansing processes. The results indicate that the developed Data Warehouse is capable of integrating data consistently and supporting multidimensional analysis. Data visualization using Tableau Public reveals a correlation between Body Mass Index (BMI) and diabetes status, where patients diagnosed with diabetes exhibit a higher average BMI compared to non-diabetic patients. This implementation improves data access efficiency and facilitates the identification of health risk patterns. Therefore, the proposed Data Warehouse can serve as a foundation for a healthcare analytics system that supports data-driven strategies for diabetes prevention and management.]]>
