  In the contemporary data-driven landscape, ensuring data quality (DQ) is
crucial for deriving actionable insights from vast data repositories. The
objective of this study is to explore the potential for automating data quality
management within data warehouses as data repository commonly used by large
organizations. By conducting a systematic review of existing DQ tools available
in the market and academic literature, the study assesses their capability to
automatically detect and enforce data quality rules. The review encompassed 151
tools from various sources, revealing that most current tools focus on data
cleansing and fixing in domain-specific databases rather than data warehouses.
Only a limited number of tools, specifically ten, demonstrated the capability
to detect DQ rules, not to mention implementing this in data warehouses. The
findings underscore a significant gap in the market and academic research
regarding AI-augmented DQ rule detection in data warehouses. This paper
advocates for further development in this area to enhance the efficiency of DQ
management processes, reduce human workload, and lower costs. The study
highlights the necessity of advanced tools for automated DQ rule detection,
paving the way for improved practices in data quality management tailored to
data warehouse environments. The study can guide organizations in selecting
data quality tool that would meet their requirements most.
