Data Quality

Concepts, Methodologies and Techniques

verfasst von: Carlo Batini, Monica Scannapieca

Verlag: Springer Berlin Heidelberg

Buchreihe : Data-Centric Systems and Applications

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

1. Introduction to Data Quality

1.6 Summary

In this chapter we have perceived that data quality is a multidisciplinary area. This is not surprising, since data, in a variety of formats and with a variety of media, are used in every real-life or business activity, and deeply influence the quality of processes that use data. Many private and public organizations have perceived the impact of data quality on their assets and missions, and have consequently launched initiatives of large impact. At the same time, while in monolithic information systems data are processed within controlled activities, with the advent of networks and the Internet, data are created and exchanged with much more “turbulent” processes, and need more sophisticated management.

The issues discussed in this chapter introduce to the structure of the rest of the book: dimensions, models, techniques, methodologies, tools, and frameworks will be the main topics addressed. While data quality is a relatively new research area, other areas, such as statistical data analysis, have addressed in the past some aspects of the problems related to data quality; with statistical data analysis, also knowledge representation, data mining, management information systems, and data integration share some of the problems and issues characteristic of data quality, and, at the same time, provide paradigms and techniques that can be effectively used in data quality measurement and improvement activities.

2. Data Quality Dimensions

3. Models for Data Quality

4. Activities and Techniques for Data Quality: Generalities

4.5 Summary

In this chapter we have introduced several data quality activities, discovering that the improvement of data quality in an organization can be performed with a variety of actions and strategies. All of the activities introduced apply to data, and produce data of improved quality according to a given process. Other improvement activities can rely on processes that manipulate data, modifying the process or introducing suitable controls in the process; we will discuss them in Chapter 7.

We have also started the discussion on activities while thoroughly analyzing (i) quality composition, and (ii) error localization and correction. Finally, we have discussed cost-benefit classifications in data quality, that can be used as check lists in the process of cost and benefit allocation. For quality composition and error localization and correction we introduced a spectrum of techniques for several possible cases, while for cost/benefit classifications we compared the different approaches. In such a way, we provided a framework for analysis that allows the reader to choose the specific approach to adopt based on the context of use.

5. Object Identification

6. Data Quality Issues in Data Integration Systems

7. Methodologies for Data Quality Measurement and Improvement

8. Tools for Data Quality

9. Open Problems

9.5 Conclusions

In this last chapter we outlined the future development of the data quality research area. In addition to what was presented in this book, in the next ten years there will probably be a widespread increase in contributions in the area, with new paradigms and approaches. Indeed, information is a “plastic” concept and resource, that can hardly be encapsulated into fixed models and techniques. We use textual information to write poetry, facial information to express emotions, musical information to compose or listen to operas. What does it mean that a note in a symphony is executed wrong? It is not easy to formalize this concept, and, probably, it is not useful, since a huge number of phenomena, luckily for us, have to be perceived, and will continue to be perceived, on the basis of our feelings and emotions.

Backmatter

Titel: Data Quality
verfasst von: Carlo Batini
Monica Scannapieca
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-540-33173-5
Print ISBN: 978-3-540-33172-8
DOI: https://doi.org/10.1007/3-540-33173-5

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