Everyone agrees that data quality is important, but that doesn’t make them willing to pay for it. Any manager asked to approve a significant data quality project will rightly want to understand its ROI. Sometimes the justification is a simple cost reduction: fewer duplicate mailings, fewer misdirected shipments, fewer customer service transactions to correct mistakes. But often the benefit comes from higher revenue: better data will allow more effective promotions or more precise (that is, higher) pricing. Data management professionals are frequently unfamiliar with the details of such analyses. Even the business counterparts who are supposed to provide the necessary input may not know how to structure them to satisfy financial gatekeepers.

The challenge usually lies with the value calculation - the “R” in ROI. After all, investment is not much different from any other systems project. But how do you estimate the value of incremental revenue - or, indeed, what that revenue might be? Following are several real-world projects that involve benefits from improved data quality. Although they cover just a handful of the possible situations, they may inspire insights that apply to your own business.

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