This paper has four interlocking goals: First, to emphasize the critical importance of high-quality data if decision makers are to trust their data warehouses and fully utilize them. Second, to contrast the competing approaches for managing data quality. Respectively, these aim to find and fix (clean up) errors and prevent errors at their sources. The second approach, preventing errors, is clearly superior, yielding orders of better data that can be trusted for even the most critical decisions.

Preventing errors at their sources requires a single-minded focus on the business processes that create data. Therefore, it is primarily a business, not technology, endeavor. Unfortunately, too many enterprises elect the find-and-fix errors approach, perhaps reasoning that "since the data is in the warehouse, it must be the responsibility of the warehouse manager." So, the third goal of this paper is to clarify for business managers that it is they, not IT, who must lead the data quality program. Finally, the fourth goal is to help business managers understand what they must do to improve data quality.

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