This paper presents an approach to clearly identify the opportunities for increased monetary and non-monetary benefits from improved Data Quality, within an Enterprise Architecture context. The aim is to measure, in a quantitative manner, how key business processes help to execute an organization's strategy, and then to qualify the benefits as well as the complexity of improving data, that are consumed and produced by these processes. These findings will allow to clearly identify data quality improvement projects, based on the latter's benefits to the organization and their costs of implementation. To facilitate the understanding of this approach, a Java EE Web application is developed and presented here.
As business processes have become increasingly automated, data quality becomes the limiting and penalizing factor in the business service's overall quality, and thus impacts customer satisfaction, whether it is an end-user, an institutional partner or a regulatory authority. The available research that is related to business services' quality paid very little attention to the impact of poor data quality on good services delivery and customer satisfaction, and to the calculation of the optimal level of data quality. The aim of this paper is to present a customeroriented approach that will help to understand and analyze how an organization business service's overall quality is linked to the quality of upstream business processes and of data objects in use. This paper also introduces a calculation framework that allows the identification of an optimal level of data qualitydata accuracy dimension in the case of this papertaking into account the business processes' execution accuracy and data accuracy.
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