Data has become one of the most valuable assets within every modern organization, yet its value depends entirely on its quality. Decisions based on incomplete, inconsistent, duplicated, or inaccurate information can lead to operational inefficiencies, regulatory challenges, dissatisfied customers, and missed business opportunities.
While organizations continue to invest heavily in analytics, artificial intelligence, cloud platforms, and digital transformation initiatives, many still struggle with a fundamental problem: ensuring that the data driving these technologies is trustworthy.
This book provides a practical, hands-on guide to understanding and implementing enterprise data quality. Rather than focusing solely on theory, it presents the principles, processes, governance practices, and technologies that enable organizations to improve the accuracy, consistency, completeness, and reliability of their information assets. Throughout these chapters, you will learn how data quality fits within the broader disciplines of data governance, master data management, reference data management, metadata management, and enterprise architecture.
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