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Data Quality

Concepts, Methodologies and Techniques

By Carlo Batini , Monica Scannapieco

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Poor data quality can seriously hinder the effectiveness of organizations and businesses. This book offers state of the art understanding of the issues surrounding data quality, and sound practical advice for analyzing and improving data quality in the real world.

Full Description

  • ISBN13: 978-3-5403-3172-8
  • 284 Pages
  • User Level: Science
  • Publication Date: September 27, 2006
  • Available eBook Formats: PDF
  • eBook Price: $99.00
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Full Description
Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the 'Data Quality Act' in the USA and the 'European 2003/98' directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone – researchers, students, or professionals – interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.
Table of Contents

Table of Contents

  1. 1. Introduction to Data Quality.
  2. 2. Data Quality Dimensions.
  3. 3. Models for Data Quality.
  4. 4. Activities and Techniques for Data Quality
  5. 5. The Object Identification Process.
  6. 6. Data Quality Issues in Data Intregration Systems.
  7. 7. Methodologies for Data Quality Measurement and Improvement.
  8. 8. Tools for Data Quality.
  9. References.
  10. Index.
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