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.
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 confidently used across the enterprise, regardless of business application and system. 1. Profiling As the first line of defense for your data integration solution, profiling data helps you examine whether your