Carlo Batini
Cinzia Cappiello
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Abstract
The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
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Index Terms
- Methodologies for data quality assessment and improvement
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Reviews
Andreas E. Schwald
Data quality comprises a wide subject area, with a variety of dissimilar issues. It is far from trivial to compile a comprehensive survey of the field. This treatise on data quality assessment and improvement presents 13 methodologies, over 50 pages, and lists 92 references up to the year 2007. It aims to provide a "systematic and comparative description along several dimensions, including phases and steps, strategies and techniques, data quality dimensions, types of data, and types of information systems." The paper may be unsatisfactory and too shallow for an advocate of a particular methodology. However, it can be quite helpful for a quick overview, especially for those who are looking for improvement, implementation advice, or new ideas. It covers a wide field and stimulates the interest of the reader?although, in most cases, a reference is needed to obtain an answer to a specific question or for an in-depth treatment of a topic. This is a noteworthy effort that sums up a great deal of information from a rather heterogeneous field. It covers several publications that might not be available in a library of modest size, thereby bringing this information to the attention of a wider reader community. However, whether quality is in the eyes of an observer or in measurable attributes of an object remains an open question. Online Computing Reviews Service
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