The data quality working group investigated as many dimensions of data quality as possible. It resulted in a longlist of 60 dimensions.
The working group provided each dimension of data quality with a proper definition. The definitions meet the requirements of the ISO 704 standard for definitions. The definitions are therefore standardised.
Dimensions associated with a data concept
Furthermore, the working group found out, that it is not enough to link the dimension only to the term ‘data’, but that it is better to be more specific about which data concepts are concerned. Therefore, a distinction is made between different data concepts such as data value, data records and data attributes. In this way, a distinction can be made, for instance, between the completeness of data values, data records and data attributes. This avoids an impossible task of classifying dimension, because there is no such thing as an ontology of dimensions..Now dimensions can be subdivided according to the data concepts they belong to, e.g., all dimensions belonging to ‘data value’.
All dimensions of data quality and its definitions in one table
Don’t forget to hover over the blue boxed.
Dictionary of dimensions of data quality and data concepts
The following underlying research reports are the basis for above results.
The full research report: Dimensions of Data Quality (DDQ)
Reaearch report about data concepts: Data concept system for dimensions of data quality
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