Data governance addresses data retention policies by establishing clear rules, processes, and accountability for how long data is stored and when it should be deleted. It ensures retention practices align with legal requirements, business needs, and risk management goals. For example, a governance framework might define roles like data stewards to oversee retention schedules, document retention periods for specific data types (e.g., customer logs vs. financial records), and enforce automated deletion workflows. This prevents scenarios where data is kept indefinitely, which could lead to compliance violations or unnecessary storage costs.
To implement retention policies, data governance provides technical guidelines for developers. This includes defining how data lifecycle management tools (like AWS S3 lifecycle rules or database retention flags) should be configured. For instance, a policy might require user activity logs to be encrypted, archived for 90 days, and then purged. Developers would codify these rules into infrastructure-as-code templates or scripts, ensuring consistency across systems. Governance also clarifies exceptions: medical data under HIPAA might require 6-year retention in a specific encrypted format, while marketing cookies might need deletion after 12 months. These technical specs reduce ambiguity and help avoid manual errors.
Finally, governance enforces compliance through monitoring and audits. Automated checks (like cron jobs or cloud-native tools) verify that data is deleted on schedule, while audit logs track retention actions for regulatory reporting. If a system fails to purge expired data, alerts notify engineers to investigate. Governance also mandates periodic reviews: a team might audit backups annually to ensure old datasets aren’t retained beyond policy limits. This structured approach ensures retention isn’t just a one-time setup but an ongoing process, minimizing legal risks and keeping storage costs predictable for technical teams.