<![CDATA[In the rapidly growing tourism industry, efficient data management is crucial for strategic decision-making. This study focuses on the design and implementation of a data warehouse to optimize the monitoring dashboard for hotel bookings on an application X, an online hotel booking platform, a product of PT. XYZ. Along with the increase in volume and complexity of booking transaction data, there is an urgent need for an efficient data management system that can provide insights quickly and accurately. To address this, the study uses Kimball's Nine-Step Methodology and implements a star schema, which simplifies complex data relationships and improves query performance. The ETL (Extract, Transform, Load) process is applied to ensure accurate extraction, transformation, and loading of data from operational systems into the data warehouse, thereby guaranteeing consistency and accuracy for analysis. Tools such as SQL Server Management Studio 2022, Visual Studio 2019, and SQL programming were used to develop the data warehouse and the star schema. The results of the study show that the star schema, with a central fact table and surrounding dimension tables, effectively optimizes data processing and query speed. The system is scalable, allowing for future expansion without significant changes, and provides a robust platform for business intelligence. Recommendations include developing dimension analysis, optimizing the ETL process, and integrating predictive analytics to enhance decision-making. Overall, this study provides a structured data warehouse design that significantly improves PT. XYZ's ability to process, analyze, and visualize hotel booking data for strategic decision-making. Keywords: Data Warehouse, Extract Transform Load (ETL), Dashboard, Nine-Step Kimball, SQL.]]>
