Master Data Quality Checks: Essential Strategies for Data Engineers

Data quality checks are essential for preventing financial losses and compliance failures. Key check types include completeness, uniqueness, consistency, validity, and timeliness evaluations. Effective strategies involve defining clear metrics, automating checks within ETL pipelines, conducting regular audits, tracking data lineage, and fostering a culture of data accuracy. Tools like Great Expectations, Monte Carlo, and ML-based anomaly detection can help automate and scale these practices. Real-world examples include Equifax's $725K settlement and Unity Technologies' $110M loss from poor data quality.