Design Data Retention Policies

Key Concepts

• Data Classification
• Legal and Regulatory Requirements
• Business Continuity and Disaster Recovery
• Data Lifecycle Management
• Data Archiving and Deletion

Data Classification

Data classification involves categorizing data based on its sensitivity, importance, and usage. This helps in determining the appropriate retention period and storage requirements. Common classifications include public, internal, confidential, and restricted data.

Example: A financial institution might classify customer account information as confidential, requiring strict access controls and longer retention periods compared to public marketing materials.

Legal and Regulatory Requirements

Legal and regulatory requirements dictate how long certain types of data must be retained. Compliance with laws such as GDPR, HIPAA, and Sarbanes-Oxley Act is crucial. These regulations often specify retention periods, data protection measures, and the conditions under which data can be deleted.

Example: Under GDPR, personal data must be retained only as long as necessary for the purposes for which it was collected. A healthcare provider must retain patient records for a specified period and ensure they are securely stored and accessible only to authorized personnel.

Business Continuity and Disaster Recovery

Data retention policies must support business continuity and disaster recovery efforts. This involves keeping backups of critical data and ensuring they are stored in a secure, offsite location. Retention periods for backups should align with the organization's recovery time objectives (RTO) and recovery point objectives (RPO).

Example: A retail company might retain daily backups of transactional data for 30 days to ensure quick recovery in case of data loss, while retaining monthly backups for historical analysis.

Data Lifecycle Management

Data lifecycle management involves managing data from creation to deletion. This includes defining retention periods, archiving data that is no longer actively used, and ensuring that outdated data is securely deleted. Azure provides tools like Azure Data Lake Storage and Azure Blob Storage with lifecycle management policies to automate these processes.

Example: An e-commerce platform might move older customer order data to cold storage after one year and delete it after five years, while keeping recent orders in hot storage for quick access.

Data Archiving and Deletion

Data archiving involves moving data to a long-term storage solution when it is no longer actively used but still required for compliance or historical purposes. Deletion involves securely removing data that is no longer needed. Azure offers features like soft delete and immutable storage to ensure data is securely archived and deleted.

Example: A media company might archive old video content in Azure Blob Storage with immutable storage to prevent accidental deletion, while deleting content that is no longer under copyright protection.