Data Warehouse Design for Enterprise Decision Support Systems

Abstract

Enterprise decision support systems require reliable, integrated, and well-structured data to support strategic and operational decisions across business functions. Data warehouse design provides the foundation for consolidating heterogeneous enterprise data, improving reporting consistency, and enabling historical analysis for management intelligence. Existing literature highlights dimensional modeling, ETL processes, OLAP analysis, metadata management, and data quality control as core elements of enterprise data warehousing. However, traditional designs still face challenges related to fragmented source systems, delayed data refresh, changing business requirements, and inconsistent analytical outputs. This research is important because enterprises need scalable, accurate, and traceable decision support environments that can convert large volumes of operational data into useful insights. This article discusses data warehouse architecture, source integration, schema design, ETL workflow, fact and dimension modeling, query optimization, and analytical reporting layers. The study concludes that an effective data warehouse improves decision accuracy, reduces redundancy, supports cross-functional visibility, and strengthens enterprise planning.