Abstract
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.
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Vaziri, R., Mohsenzadeh, M. (2012). Towards a Practical “State Reconstruction” for Data Quality Methodologies: A Customized List of Dimensions. In: Wyld, D., Zizka, J., Nagamalai, D. (eds) Advances in Computer Science, Engineering & Applications. Advances in Intelligent and Soft Computing, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30157-5_82
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DOI: https://doi.org/10.1007/978-3-642-30157-5_82
Publisher Name: Springer, Berlin, Heidelberg
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