Enterprise Data Quality Management

About this course

This course provides a comprehensive introduction to the principles, methodologies, and tools used to ensure high-quality data within modern organizations. It focuses on the assessment, measurement, and improvement of data quality across the data lifecycle.Students will explore key data quality dimensions—such as accuracy, completeness, consistency, timeliness, Currency , Uniqueness and validity—and learn how poor data quality impacts organizational reputation ,  regulatory reports, business operations, analytics, and decision-making. The course covers techniques for data profiling, cleansing, standardization, and validation, as well as the design of data quality metrics and monitoring systems.In addition, the course introduces data governance frameworks, roles, and policies that support sustainable data quality management. Students will examine challenges in data integration, ETL processes, and master data management, alongside ethical and regulatory considerations related to data usage.Through practical exercises and case studies, students will gain hands-on experience using tools and techniques to identify data issues, implement corrective actions, and maintain reliable datasets in real-world scenarios.This course introduces concepts and practices for managing, monitoring and improving data quality. Topics include data profiling, cleansing, validation, quality metrics, root cause analysis technics and governance. Students gain practical experience in identifying and resolving data issues to support accurate and reliable decision-making.

Course objectives

• to provide foundational knowledge in data quality principles
• to equip students with hands-on experience in data quality techniques
• to introduce data governance frameworks and roles