Data Quality Management for BI is the systematic process of ensuring that data used in business intelligence systems meets defined standards of accuracy, completeness, consistency, and reliability. It encompasses profiling, validation, cleansing, monitoring, and governance practices that transform raw data into trustworthy information for decision-making. In 2026, data quality has reclaimed the top priority position in BI initiatives, surpassing even AI hype, as organizations recognize that poor data quality undermines analytics credibility and costs businesses an average of $12.9 million annually. The key mental model: data quality is not a one-time cleanup but a continuous feedback loop—profile to discover issues, validate to prevent them, monitor to detect drift, remediate to fix problems, and govern to sustain improvements across the entire data lifecycle.