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.
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