This content by Dataiku is a comprehensive guide highlighting the importance of data quality in ML projects and its impact on business outcomes. It provides best practices for controlling data quality, including engaging leaders, investing in data quality infrastructure, establishing accuracy metrics, defining what “quality” means to the organization, providing accountability for data accuracy, and establishing a process for issue management control.

What will you learn when you read this document?

• Why data quality is critical in machine learning projects and how does it affect business outcomes?
• What are the best practices for controlling data quality in an organization?
• How can you create metrics about accuracy to get quality data?
• What is the role of the data manager in managing data quality and how can it contribute to better data governance?
• How can you effectively manage data quality issues and provide a robust process for problem management control?