Undertaking a project to rationalize, cleanse and maintain entity data across an enterprise can be a daunting prospect. My columns this month and next will provide a high-level framework for establishing priorities and creating metrics that you can use to implement an entity data quality enhancement program.

The information that you have about businesses - customers, prospects, vendors, counterparties - across your enterprise is actually a bundle of facts, or attributes, about these businesses. Within your company’s databases, you may have one or several company names, one or several addresses, payment histories and relationship information. Customer service may know that the chief marketing officer at Company D does not want to be called, but sales may not have this important do-not-call information. Tackling the rationalization, cleansing and active maintenance of all this disparate entity data cannot be done as one big project. It would take too long and cost too much before any real benefits were achieved, and maintenance is a never-ending process. You need to make choices about which attributes to enhance first. What is the best way to establish priorities? And, once you begin to implement data quality projects, you will need to measure your success. What are the right metrics for each attribute type?

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