Since how data quality is defined has a significant impact on how data quality is perceived, measured, and managed, in this post I examine the two most prevalent perspectives on defining data quality, real-world alignment and fitness for the purpose of use, which respectively represent what I refer to as the danger of data myopia and the challenge of business relativity.
Whether it’s an abstract description of real-world entities (i.e., master data) or an abstract description of real-world interactions (i.e., transaction data) among entities, data is an abstract description of reality. The creation and maintenance of these abstract descriptions shapes the organization’s perception of the real world, which I philosophically pondered in my post “Plato’s Data.”
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