COMPEL Glossary / data-minimization
Data Minimization
Data minimization is a core data protection principle, mandated by GDPR and adopted by many other privacy frameworks, requiring that organizations collect and retain only the personal data that is strictly necessary for a specific, stated purpose.
What this means in practice
It restricts the common AI development practice of collecting as much data as possible in case it might be useful. For organizations training AI models, data minimization creates a productive tension between the desire for more data to improve model performance and the legal and ethical obligation to limit data collection to what is justified by a legitimate purpose. In COMPEL, data minimization is assessed as part of the Governance pillar during Calibrate and is integrated into the data architecture design during Model, where it connects to privacy-by-design practices and purpose limitation controls.
Why it matters
Data minimization creates a productive tension between the desire for more data to improve AI model performance and the legal obligation to limit collection to what is justified. Organizations that ignore this principle face regulatory penalties under GDPR and other privacy laws. Those that embrace it build stronger trust with customers and stakeholders while developing more focused, efficient AI systems that use data purposefully rather than indiscriminately.
How COMPEL uses it
COMPEL assesses data minimization practices as part of the Governance pillar during Calibrate, evaluating whether data collection policies align with privacy-by-design principles. During Model, data minimization is integrated into the data architecture design, connecting to purpose limitation controls. The Evaluate stage audits whether deployed AI systems comply with data minimization requirements across all active use cases.
Related Terms
Other glossary terms mentioned in this entry's definition and context.