What you'll learn

Trusted data needs controls that make requirements repeatable. In this course, Design and Implement Data Quality Controls, you will gain the ability to design controls across a data lifecycle and choose practical failure actions. First, you will explore how schema, required field, domain, range, pattern, uniqueness, and conditional checks turn requirements into working validation. Next, you will discover how referential integrity, reconciliation, record counts, aggregation checks, and workflow integration protect pipeline results. Finally, you will learn how failed-record details, control coverage, system changes, and carefully reviewed AI suggestions help improve controls over time. When you are finished with this course, you will have the skills and knowledge of data quality control design needed to make trusted data more repeatable.