Enterprise Data Quality: Strategy, Measurement, and Governance

Reliable data is essential for effective decision-making, yet legacy systems and cross-departmental silos frequently result in fragmented, inaccurate information. This text-based course provides a structured roadmap for diagnosing data issues, establishing clear quality benchmarks, and fostering sustainable data stewardship across complex teams. What you'll learn: - Understand the core dimensions of data quality, including accuracy, completeness, validity, and timeliness - Measure baseline data health using modern data profiling and metric tracking - Apply data governance principles to define roles, responsibilities, and clear accountability - Configure automated validation rules and modern data observability patterns - Implement scalable root-cause remediation workflows to resolve recurring data debt - Build cross-functional alignment to sustain long-term data quality initiatives The course begins with fundamental terminology and foundational quality frameworks before guiding you through real-world implementation strategies and operational workflows. Designed for beginners, data analysts, project leads, and operations specialists, this guide requires no prior technical prerequisites. Start reading today to build trusted, resilient data practices in your organization.