What method should a data analytics team implement for sharing dashboard analysis while restricting access to external product owners?

Utilizing dataset rules with row-level security in Amazon QuickSight is the most effective method for sharing dashboard analysis while managing access restrictions. This approach allows the analytics team to create specific access controls at the row level based on user attributes.

By implementing dataset rules, the team can define which data each user or group can access, ensuring that external product owners only see the information relevant to them. This capability is crucial in scenarios where sensitive data must remain confidential and access needs to be tailored according to the roles or needs of the audience.

In contrast, separating the data and using IAM policies for secure authorization can be effective for overall access control but may not provide the granularity needed to restrict data visibility effectively at the dashboard level. A manifest file can help enforce security, but it's not as integrated into the analytical workflow as the row-level security capabilities of QuickSight. Finally, using S3 bucket policies to control access is generally more suitable for managing file access at a storage level rather than directly related to data analysis visualizations. This doesn’t offer the same level of detailed control as row-level security for dashboard sharing.