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When the correlation between poor data quality and poor business performance isn’t measured in a tangible way, data quality can be misperceived as a technical activity performed for the sake of the data, instead of an enterprise-wide initiative performed to provide data-driven solutions for business problems.

The common mistake is taking a data-myopic approach to data quality metrics, i.e., creating metrics that reflect the quality of the data in isolation. Without understanding how the organization is using its data, and how data quality affects business results, data cannot be called a corporate asset. Data is an asset only if the organization can qualify and quantify its value by connecting its usage to business objectives.