Data Mart Design for Departmental Reporting Systems

Abstract

Data mart design is important for departmental reporting systems because individual business units require focused, relevant, and easily accessible data for routine analysis and performance monitoring. A data mart provides a smaller, subject-oriented subset of enterprise data that supports departments such as finance, sales, human resources, procurement, and operations. Existing literature highlights dependent data marts, independent data marts, dimensional modeling, ETL processes, fact tables, dimension tables, aggregation, and reporting layers as major components of data mart development. However, many organizations still face challenges such as inconsistent departmental data, duplicate reporting structures, limited integration with enterprise warehouses, slow report generation, and poor alignment between data models and user requirements. This research is important because department-level decision-making requires accurate, timely, and customized reporting without overloading central enterprise data systems. This article discusses data mart design for departmental reporting systems, focusing on requirement analysis, data source selection, schema design, ETL workflow, aggregation strategy, access control, and report optimization. The study concludes that an effective data mart improves reporting speed, strengthens departmental decision-making, reduces unnecessary data complexity, and supports more efficient enterprise information delivery.