Part 6/11:

• Data Engineers: Traditionally responsible for data pipelines, but increasingly expected to embed quality checks.

• Data Analysts: Often burdened with cleaning and preparing data for exploration, highlighting the need for proactive quality practices.

• Business Leaders: Their engagement ensures that data considerations align with strategic objectives and compliance.

The evolution of data quality approaches over the past decades reflects a shift from reactive, manual validations to automated, proactive systems. Pre-2010, reliance on manual checks and static validations kept data issues hidden. Post-2010, automation, monitoring tools, and early anomaly detection introduced preventive measures, aligning data management closer to operational resilience.