Philosophers, marketing executives, linguists and scientists have struggled with the distinctions between data, information and knowledge for decades if not centuries. The suggestion is to take a practical approach to defining these distinctions, but to do so in way that preserves consistency with both logic and experience. We know that lack of data quality costs money misdirected mail is returned, effort is wasted, rework is incurred, sales and customers are lost and inventory outages occur. Quality implies differences, differences imply distinctions of value and distinctions of value imply market value. Market value implies the dollar value. Like so many things, information quality is a bootstrap operation requiring iteration, a process of learning from one's mistakes and commitment to business results.
When stated out of context, "data quality" is a misnomer. Data in itself is meaningless, data is what is given it is basic raw material. Whether unstructured or structured content, it is data. Data itself is worthless. It is what you do with the data that has value. Data is the content; and when it is structured in such a way as to reduce uncertainty, then it has value as information. Thus, data plus structure produces information. Information provides differences and distinctions that reduce uncertainty.
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