Dataotiminisering

Dataotiminisering is a term used in data governance and privacy discourse to describe the systematic reduction of data collection, retention, and processing to what is strictly necessary for a stated purpose. The concept emphasizes minimizing personal data exposure, enhancing security, and reducing operational costs by avoiding unnecessary data handling. While not widely adopted as a formal standard, dataotiminisering aligns with the broader principle of data minimization found in privacy regulations and privacy-by-design practices.

Core principles include purpose limitation, minimal data collection, on-device or edge processing when feasible, timely deletion, and rigorous access controls. Techniques used to implement dataotiminisering include data minimization (collecting only what is needed), anonymization and pseudonymization, data aggregation, data retention policies, encryption, and, where possible, the use of synthetic data or differential privacy to enable analysis without exposing individuals.

Applications span privacy-preserving analytics, improved security posture, and cost reductions in data storage and processing. In regulated contexts, dataotiminisering supports compliance with privacy laws by reducing data footprints and exposure risk. Critics argue that aggressive minimization can degrade data utility, hamper certain analyses, or require complex governance and trade-offs between privacy and insight. Proponents counter that a balanced approach can preserve analytical value while mitigating risk, especially when combined with modern privacy-preserving techniques.

See also data minimization, privacy by design, differential privacy, and secure multi-party computation.