Quality
Organizations are conscious of data quality problems. Nevertheless, efforts generally
focus on data accuracy, ignoring many other attributes and important quality dimensions
(Wang & Strong, 1996). Thus, quality validation and verification techniques
are still required. Usually, these techniques concentrate only on software and assume
that external agents provide the data (Bobrowski et al., 1999). Poor information
quality is due to several causes: (1) Problems in the processes: to understand the
processes that generate, use, and store the data, it is essential to understand data
quality. In an organization, the owners of the processes must be responsible for the
quality of the data they produce or use. (2) Problems in the information systems:
often related to poor system development (incomplete documentation or systems
that have been extended beyond their original intention). (3) Problems of policies
and procedures: a policy about data must cover security, privacy, inventory of the
information that is controlled, or data availability. (4) Problems in data design: more
often than not, data are used for tasks they were not defined for.
Data.Quality.Dimensions.
There are basically two ways of defining data quality: the first one uses a scientific
approach and defines data quality dimensions rigorously, classifying them as dimensions
that are or are not intrinsic to an information system (Wang, Storey, & Firth,
66 Vaisman
Copyright ?© 2007, Idea Group Inc.
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