Data quality is more than just data cleansing or address correction--it involves defining data expectations along defined dimensions that lead to quantifiable measurements for reviewing and assessing conformance to business information needs. Instituting data quality management and control requires well-defined processes for inspection, monitoring, and the ability to take specific actions to isolate root causes and eliminate the introduction of flawed data into the environment. Once the appropriate processes have been defined, the best way to achieve a sustainable data quality management program is to incorporate the right data quality tools and technology--data profiling, parsing and standardization, linkage/matching, and measurement and monitoring.