Enterprise data quality management can only become a universal practice if it is transformed from the specialized practice of a core group of data stewards into something that all knowledge workers take seriously and implement rigorously in the context of their jobs. In a self-service world, the opportunities for users to negatively impact data quality are growing, and data cannot be kept continually correct, current, consistent, and compliant unless best practices are everyone’s responsibility.

TDWI research has found that enterprises implement data quality management more successfully if responsibility for it is not limited to data stewards but also embraced and implemented by all knowledge workers as a core data literacy competency.

Join Fern Halper & David Loshin from TDWI and James Borlase from Snowlow as they discuss the following key topics:

• Why data quality management is so difficult and how it is evolving
• People, processes, and tools needed for effective data quality management
• How to transform data quality management into a universal job requirement for all knowledge workers

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