Towards a Practical “State Reconstruction” for Data Quality Methodologies: A Customized List of Dimensions

  • Reza Vaziri
  • Mehran Mohsenzadeh
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


Data quality (DQ) has been defined as “fitness for use” of the data (also called Information Quality). A single aspect of data quality is defined as a “dimension” such as “consistency”, “accuracy”, “completeness”, or “timeliness”. In order to assess and improve data quality, “methodologies” have been defined. Data quality methodologies are a set of guidelines and techniques that are designed for assessing, and perhaps, improving data quality in a given application or organization. Most data quality methodologies use a pre-defined list of dimensions to assess the quality of data. This pre-defined list is usually based on previous research and may not be related to the specific application at hand. As a prelude (or state reconstruction phase) for methodologies, a useful list of dimensions specific to the current application or organization must be collected. In this paper we propose a state reconstruction phase in order to achieve that.


Data Quality Dimensions Methodology 


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© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer Science, Science Research BranchAzad University of IranTehranIran

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