How do you manage data lifecycle and retention policies in data science?

Managing data lifecycle and retention policies in data science involves a comprehensive approach to data management that ensures data is handled properly throughout its lifecycle. Here are some key steps in managing data lifecycle and retention policies:

• Define Data Retention Policies: Establish clear guidelines on how long data should be retained based on regulatory requirements and business needs.
• Implement Data Governance Frameworks: Develop frameworks that govern data usage, access, and security to ensure compliance and data quality.
• Utilize Data Backup and Archiving Strategies: Implement backup and archiving solutions to protect data from loss or corruption and ensure data availability.
• Monitor Data Usage: Track data usage patterns, access levels, and changes to data to identify potential risks or compliance issues.

By following these steps and implementing robust data lifecycle and retention policies, organizations can effectively manage their data assets in data science projects.