Without data that is reliable, accurate, and updated, organizations can’t confidently distribute that data across the enterprise, leading to bad business decisions. Faulty data also hinders the successful integration of data from a variety of data sources. But with a sound data quality methodology in place, you can integrate data while improving its quality and facilitate a master data management application—at low cost.
be uncovered, such as blank phone numbers or addresses. Or certain data may be incorrect, such as a record of a customer indicating he/she lives in the city of Wisconsin, in the state of Green Bay. Setting in place a process to fi x these data quality issues is important for the success of MDM, and involves six key tasks: profiling, cleansing, parsing/standardization, matching, enrichment, and monitoring. The end result — a process that delivers clean, consistent data that can be distributed and