What’s the best approach for detecting data quality issues automatically?

Example Concept: Data quality can be monitored using automated scripts that check for missing values, outliers, duplicates, and inconsistencies. Techniques such as statistical profiling, rule-based validation, and machine learning models for anomaly detection can be employed. Tools like Great Expectations or Apache Griffin provide frameworks for defining and executing these checks within data pipelines.