HumenhukCIPP/E Study Guide
Ch 6.4 - Data minimisation

Data minimisation

Reviewed by Victor Humenhuk. Published . Reviewed .

Data minimisation means collecting and processing only data that is relevant, necessary and adequate for the purpose - collect only what you really need. It is implemented through two concepts: necessity and proportionality. A 'save-everything' approach is disproportionate and breaches the principle. A useful starting point is asking whether anonymous or anonymised data could achieve the purpose instead of personal data.

The EDPS puts it simply: collect only the personal data you really need. Apply two tests - necessity (is each field actually required?) and proportionality (is the amount and intrusiveness appropriate?). A 'save-everything' approach is disproportionate.

  1. Could the purpose be met with anonymous (fake) data? If so, use that.
  2. If not, could anonymised data (stripped of all identifiers) work?
  3. If only personal data works, collect only necessary fields (e.g. age range instead of full date of birth).
  4. Check proportionality: prefer less-intrusive means; avoid excessive volume.
Spain's AEPD on biometrics

The AEPD found fingerprint, keystroke and facial-recognition systems disproportionate where less-intrusive means could achieve the same purpose (e.g. validating a student's identity in an online exam).

Don't confuse with storage limitation

Data minimisation is about how much you collect (relevance/necessity/proportionality). Storage limitation is about how long you keep it. Same instinct ('don't hoard'), different axis.

Key terms - quick answers

What is “Data minimisation”?
Limit collection/processing to data that is relevant, necessary and adequate to the purpose - collect only what you really need.
What is “Necessity”?
The data must be suitable and reasonably required to attain the purpose; if a field could be dropped and the purpose still met, it isn't necessary.
What is “Proportionality”?
The amount and intrusiveness of data must be appropriate to the purpose; less-intrusive alternatives should be preferred.
What is “Anonymised data”?
Data stripped of all unique identifiers; not personal data. Pseudonymous data is NOT anonymous.

Sources and study method

This independent lesson uses active recall, spaced retrieval and scenario practice. Read the full study method.