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13min chapter
#222 [Radar Recap] Scaling Data Quality in the Age of Generative AI
DataFramed
Scaling Data Quality and Addressing Cultural Issues
This chapter explores the challenges of scaling data quality in central data warehouses, emphasizing the importance of verification mechanisms like direct sampling and a three-step framework for awareness, cure, and prevention. It discusses creating awareness of data quality through tools and collaboration between business and data producers, stressing the need for preventive measures like data contracts. The chapter also highlights the significance of addressing cultural issues alongside technical aspects to improve data quality and achieve data trust within organizations.
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