Anti-patterns for data lifecycle management

Lack of data protection measures: Lax encryption, data access controls, backup policies, and poorly defined recovery objectives contribute to data vulnerability and can lead to regulatory non-compliance. Automated backup, encryption mechanisms and comprehensive disaster recovery plans are critical in maintaining data availability and minimizing downtime during recovery processes.

Inadequate data classification practices: Accurate data classification plays a role in managing data access and ensuring the right stakeholders have access to the appropriate data. Manual or non-existent data classification could create vulnerabilities, possibly leading to misplacing data or granting unauthorized individuals access to sensitive data. An automated data classification approach, potentially leveraging AI/ML tools, can reduce human error and increase efficiency, ensuring data is consistently and correctly labeled according to its sensitivity.

Unrestricted data access: Sharing data without proper governance can expose your organization to security risks like data breaches, loss of sensitive information, or violations of data sovereignty laws. You should manage and restrict access to shared data, provide a single source of truth through centralized data lakes, and use "clean rooms" for collaboration outside of the organization's boundaries.

Reliance on manual data retention and disposal: Manual handling of data retention and disposal processes can lead to human error, missed deadlines, non-compliance, and inefficient data management. Retaining data indefinitely is also not a good options, as it can lead to increased storage costs, potential non-compliance with data privacy laws, and an increased risk of data breaches. Automate data retention enforcement to help ensure compliance and efficient data management to reduce costs and improve operational efficiency.