A hybrid approach to assessing data quality

Various techniques have been proposed to enable organizations to initiate procedures to assess and ultimately to improve the quality of their data. The utility of these assessment techniques (ATs) has been demonstrated in different organizational contexts. However, while some of the ATs are geared towards specific application areas and are often not suitable in different applications, others are more general and therefore do not always meet specific requirements. To address this problem we propose the Hybrid Approach to assessing data quality, which can generate usable ATs for specific requirements using the activities of existing ATs. A literature review and bottom-up analysis of the existing data quality (DQ) ATs was used to identify the different activities proposed by each AT. Based on example requirements from an asset management organization, the activities were combined using the Hybrid Approach in order to generate an AT which can be followed to assess an existing DQ problem. The Hybrid Approach demonstrates that it is possible to develop new ways of assessing DQ which leverage the best practices proposed by existing ATs by combining the activities dynamically.

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