Foundations of Data Quality: The TDQ Framework

In a world driven by data, poor data quality leads to flawed insights, wasted resources, and compromised decision-making. Learning how to systematically evaluate, monitor, and trust your data is the first step toward successful data analysis and governance. This course guides you through the Total Data Quality (TDQ) Framework, a structured approach to assessing data from both designed sources, such as surveys, and gathered sources, such as administrative or sensor data. You will transition from guessing about data reliability to systematically measuring representation and measurement errors, while integrating modern data observability concepts to keep your data pipelines clean. What you'll learn: - Understand the fundamental differences between designed and gathered data. - Analyze the measurement and representation dimensions of the TDQ framework. - Identify key threats to data quality, including coverage, sampling, and nonresponse errors. - Apply data profiling techniques to assess dataset completeness and consistency. - Implement modern data quality validation rules to catch errors early in the data lifecycle. - Configure programmatic checks to monitor data health and prevent pipeline failures. Starting with foundational data quality definitions and the core TDQ dimensions, the course progresses through practical scenarios, conceptual breakdowns, and written validation examples. You will learn how to diagnose data issues and establish robust quality checks. This course is designed for beginner data analysts, database administrators, and aspiring data engineers who want to ensure their analyses are built on a solid, reliable foundation. No prior data quality experience is required. Start building a reliable foundation for trustworthy data analysis today.