Data Quality Dimensions to Ensure Optimal Data Quality

Svetlana Jesiļevska
JEL codes: 
C00 - General.
Quality is more difficult to define for data, moreover the meaning of ‘quality’ depends on the context in which it is applied. Paper gives a short overview of data quality dimensions which have been collected from literature research. This paper presents some results of expert survey on data quality issues carried out by the author. The examples illustrate the fact that it is not necessary to use all the various dimensions of data quality provided by researchers, but the most essential data quality dimensions can be combined for a specific application. To support further applications of this approach, this paper contains comparison of data quality requirements to be met from statisticians and data users point of view. The empiric method (analysis of texts and documents) and the method of theoretical research (analysis of the expert survey data) are applied.
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