Slowly changing dimensions in data warehouses: an analysis through literature review

Abstract

This literature review article examines the impact and challenges posed by slowly changing dimensions (SCDs) in the context of Data Warehouses. It highlights how SCDs present unique challenges for data management and business decision making, with a focus on the need to maintain up-to-date and consistent historical data. In addition, a variety of modeling strategies used by various authors to circumvent these obstacles have been discussed, including the Kimball and Ross (2013) approach, as well as approaches such as Temporary Data Warehouses (TDW) and an emphasis on “Extract, Transform, and Load” (ETL). Each strategy is tailored to different business and data needs, offering valuable solutions to effectively address the SCD challenge and improve data quality in Data Warehouses. This study provides valuable insights for future research and practice, highlighting how strategic SCD management can empower informed decision making in the era of business analytics.