Data Minimization
The privacy principle of collecting only the personal data that is strictly necessary for a specific, stated purpose - no more, no less.
Why It Matters
Data minimization is both a legal requirement under GDPR and a best practice for analytics. Collecting less data reduces your compliance burden, security risk, and storage costs while forcing you to focus on metrics that actually matter.
Teams that practice data minimization often end up with cleaner, more actionable analytics because they are intentional about what they track rather than collecting everything and hoping to find patterns.
Common Mistakes
- -Tracking every possible event "just in case" without a clear purpose for each
- -Collecting personally identifiable information when anonymized or aggregated data would suffice
- -Not regularly reviewing and cleaning up unused tracking events
Pro Tips
- +Start your tracking plan with business questions, then work backwards to the minimum events needed
- +Audit your event schema quarterly - remove events that no one queries
- +Use aggregated or anonymized data for general trend analysis, reserving person-level tracking for specific use cases
Related Terms
GDPR
The General Data Protection Regulation - a comprehensive EU privacy law that governs how organizations collect, process, and store personal data of EU residents.
Privacy by Design
An approach that embeds data protection and privacy considerations into the design and architecture of systems and processes from the start, rather than adding them as afterthoughts.
Data Retention Policy
A formal policy defining how long different types of data are stored before being deleted or anonymized, balancing analytics needs with privacy requirements.
Event Schema
A structured definition of all tracked events in an analytics system, specifying each event's name, required and optional properties, data types, and allowed values.
See Data Minimization in action
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