Data Warehouse Maintenance Challenges in Long-Term BI Systems

Abstract

Data warehouse maintenance challenges are common in long-term BI systems where reporting needs, source systems, data volumes, business rules, and user expectations change continuously. In enterprise environments, maintenance becomes difficult when ETL jobs grow complex, dimension tables become inconsistent, historical data increases, metadata becomes outdated, and report logic changes without proper documentation. This article discusses how long-term BI maintenance affects data quality, reporting accuracy, system performance, load reliability, and user confidence. It explains the role of ETL monitoring, metadata management, data reconciliation, archive planning, performance tuning, access control, and documentation updates in maintaining warehouse stability. The article also highlights common challenges such as source system changes, slow batch processing, duplicate records, failed refresh cycles, unclear ownership, and difficulty managing historical reporting requirements. A structured maintenance approach is presented to improve data reliability, reduce reporting errors, support timely refreshes, and strengthen long-term BI governance. The study concludes that effective data warehouse maintenance improves reporting consistency, supports better decision-making, and ensures sustainable operation of enterprise BI systems.