Abstract
Article history: Received April 28, 2012 Accepted 6 July 2012 Available online July 11 2012 Today's methodologies for data assessment and improvement are considerably aimed at reducing costs. Data comprises different dimensions, each having certain methods and techniques to assess and improve data quality. One of the most controversial dimensions is data believability in which less attention has been paid by scholars and researchers, because of its ambiguous nature. This is categorized under the intrinsic data quality dimensions. The current paper offers a precise and comprehensive definition of such dimension, and provides some parameters to understand it. In order to calculate these parameters, furthermore, different methods are discussed. © 2012 Growing Science Ltd. All rights reserved.
Highlights
In recent years, data quality has been received widespread attention for different reasons in the areas of information system management and leadership
Among the most important reasons for this is the high level of costs for production, maintenance and application of poor data quality
Data quality assessment is the first step for data quality improvement
Summary
Data quality has been received widespread attention for different reasons in the areas of information system management and leadership. Among the most important reasons for this is the high level of costs for production, maintenance and application of poor data quality. Data quality can mean "fitting to use" (Juran & Gryna, 1980). The higher the data quality is, the higher the analysis accuracy is. Different methodologies use three different techniques to count up data quality variables; namely, Simple Ratio, Min-Max, and Weighted Average (Pipino et al, 2002). Data quality comprises different dimensions, which have been frequently studied; distinct methodologies apply different number of quality dimensions (Batini et al, 2009). Important and effective dimensions of data quality is "believability".
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