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.