In recent years, several process models for data quality management have been proposed. As data quality problems are highly application-specific, these models have to remain abstract. This leaves the question of what to do exactly in a given situation unanswered. The task of implementing a data quality process is usually delegated to data quality experts. To do so, they rely heavily on input from domain experts, especially regarding data quality rules. However, in large engineering projects, the number of rules is very large and different domain experts might have different data quality needs. This considerably complicates the task of the data quality experts. Nevertheless, the domain experts need quality measures to support their decision-making process what data quality problems to solve most urgently. In this paper, we propose a MetaDataRepository architecture which allows domain experts to model their quality expectations without the help from technical experts. It balances three conflicting goals: non-intrusiveness, simple and easy usage for domain experts and sufficient expressive power to handle most common data quality problems in a large concurrent engineering environment.
Blechinger, Juliane; Lauterwald, Frank; and Lenz, Richard, "Supporting the Production of High-Quality Data in Concurrent Plant Engineering Using a MetaDataRepository" (2010). AMCIS 2010 Proceedings. 95.