A Relevant, Believable Approach for Data Quality Assessment

This research proposes the use of an IPMAP for providing metadata about the source and processing of primary data, in order to enhance its believability and fitnessfor-use. We then derive a framework for assessing data quality-in-use from dual-process theories of human cognition. By applying a dual-process approach to data quality assessment, the model enables simultaneous evaluation of both objective and contextual data quality attributes. In addition to assessing the role of metadata for enhancing believability, we use our framework to investigate the data attributes of relevance, completeness, accuracy and timeliness. The model is the first to offer a theoretical explanation for the role of metadata in enhancing data quality.

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