Data Quality

The degree to which data meets the requirements of its intended use — measured across dimensions like accuracy, completeness, consistency, timeliness, and validity. Poor data quality is the most common reason AI projects fail in production. For data products, quality is not a one-time check but an ongoing system: automated validation rules, anomaly detection, freshness monitoring, and defined SLAs.