Data Governance for Data Science with Adam Wood - #578

This article discusses data governance in the context of data science, focusing on the challenges and solutions for large organizations like Mastercard. It highlights the importance of data quality, metadata management, and feature reuse, especially in a global environment with regulations like GDPR. The conversation with Adam Wood, Director of Data Governance and Data Quality at Mastercard, covers topics such as data lineage, bias mitigation, and investments in data management tools. The article emphasizes the growing importance of data governance and its impact on data science practices.

• •Data governance is crucial for data science, especially in large organizations.
• •Key aspects include data quality, metadata management, and feature reuse.
• •Addressing regional regulations and mitigating bias are important considerations.