Define Data Quality: Essential Steps for Data Engineers

A guide covering the essential dimensions of data quality (accuracy, completeness, consistency, timeliness, uniqueness, and validity) for data engineers. It outlines a six-step process for assessing and improving data quality, including conducting integrity evaluations, identifying issues, implementing validation rules, continuous monitoring, stakeholder engagement, and iterative improvement. The post also surveys tools like Talend, Informatica, Great Expectations, dbt, Collibra, and Alation, while noting that poor data quality costs organizations an average of $12.9 million annually and directly undermines AI model performance.