Data quality

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Poor data quality can significantly impede organizational efficiency and effectiveness, prompting initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive. Batini and Scannapieco provide a thorough introduction to various data quality issues. They detail different dimensions of data quality—such as accuracy, completeness, and consistency—and their relevance across types of data, including federated, web, and time-dependent data. The authors categorize data based on frequency of change, addressing stable, long-term, and frequently changing data. The book offers an extensive overview of techniques and methodologies from core data quality research and related fields, including data mining, probability theory, statistical analysis, and machine learning. This comprehensive presentation is complemented by a critical comparison of tools and methodologies, aiding readers in addressing their own quality challenges. Striking a balance between theoretical foundations and practical applications, the book is suitable for researchers, students, and professionals seeking a detailed understanding of data quality issues. It also serves as an excellent resource for introductory courses or self-study on the subject.