In this article, I propose a comprehensive architecture for capturing data quality events as well as measuring and ultimately controlling data quality in the data warehouse. This scalable architecture can be added to existing data warehouse and data integration environments with minimal impact and relatively little upfront investment. Using this architecture, it is even possible to progress systematically toward a Six Sigma level of quality management. This design is in response to the current lack of a published, coherent architecture for addressing data quality issues.
Three powerful forces have converged to put data quality concerns near the top of the list for organization executives. First, the long-term cultural trend that says, "If only I could see the data, then I could manage my business better" continues to grow. Most knowledge workers believe instinctively that data is a crucial requirement for them to function in their jobs. Second, most organizations understand that they are profoundly distributed, typically around the world, and that effectively integrating myriad disparate data sources is required. And third, the sharply increased demands for compliance mean that careless handling of data is not going to be overlooked or excused.
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Three powerful forces have converged to put data quality concerns near the top of the list for organization executives. First, the long-term cultural trend that says, "If only I could see the data, then I could manage my business better" continues to grow. Most knowledge workers believe instinctively that data is a crucial requirement for them to function in their jobs. Second, most organizations understand that they are profoundly distributed, typically around the world, and that effectively integrating myriad disparate data sources is required. And third, the sharply increased demands for compliance mean that careless handling of data is not going to be overlooked or excused.
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