Data Quality Management for Reliable Business Decisions

In today's data-driven world, poor data quality leads to costly business mistakes, unreliable analytics, and missed opportunities. Understanding how to identify, measure, and fix bad data is an essential skill for any professional working with information. This text-only course guides you from the absolute basics of data profiling to establishing modern data quality frameworks. You will gain the practical knowledge needed to ensure your organization's data is accurate, complete, and ready for decision-making. What you'll learn: - Understand the core dimensions of data quality, including accuracy, completeness, consistency, and timeliness. - Profile raw data to identify anomalies, duplicates, and missing values using structured methodologies. - Design data validation rules to automatically flag quality issues before they affect business reports. - Implement basic data observability and lineage concepts to track how data flows and changes over time. - Establish a culture of data stewardship and governance within your team or department. The course starts with foundational definitions and key terminology before guiding you through data profiling techniques, metric definition, and modern monitoring workflows. You will practice these concepts through written scenarios and structured exercises designed to simulate real-world data challenges. This course is designed for beginners, business analysts, and aspiring data professionals, with no prior technical or programming experience required. Start reading today to build a solid foundation in data quality and make your business reports truly reliable.