William McKnight will show you how to define data quality expectations, profile data against these expectations, measure data quality impact across various thresholds, and how to use these measurements to improve the organization’s bottom line. Many examples are provided including one on the value of a single accurate customer profile. Data quality is essential to business success, and we cannot improve what we cannot measure. Learn about the 11 categories for measuring data quality including referential integrity, uniqueness, cardinality, subtype/supertype constructs, value reasonability, consistent value sets, formatting, data derivation, completeness, correctness, and conformance to a clean set of values.