Executive Summary
Data minimisation is the principle of collecting only personal data that is necessary for the specified purpose and not retaining it beyond that necessity. This reduces privacy risk, simplifies compliance, and often improves user experience. This guide addresses practical approaches to implementing data minimisation across the data lifecycle.
Key Takeaways
- 1Challenge assumptions about what data is actually needed
- 2Design collection interfaces to gather only essential information
- 3Implement technical controls that prevent unnecessary collection
- 4Regularly review existing data holdings for minimisation opportunities
- 5Consider anonymisation where aggregated or anonymised data can serve purposes
1Understanding Data Minimisation
Data minimisation encompasses three dimensions: collect only necessary data, use data only for specified purposes, and retain data only as long as necessary. This creates constraints at collection, processing, and retention stages. The principle recognises that data you do not hold cannot be breached, misused, or become a compliance burden.
2Assessing Collection Necessity
Before collecting any personal data, rigorously assess whether collection is necessary.
Define the Purpose
Clearly articulate what purpose the data will serve. Vague purposes like 'improving services' do not support necessity assessment.
Question Each Element
For each proposed data element, ask whether the purpose can be achieved without it. Can we deliver the service without date of birth? Without phone number? Without address?
Distinguish Necessary from Useful
Data that would be useful is not the same as data that is necessary. Collect what is necessary; reconsider what is merely useful.
Consider Alternatives
Can the purpose be achieved with less invasive data? Can age range substitute for exact birthdate? Can postcode substitute for full address?
Document Decisions
Record necessity assessments including data elements considered, decisions made, and rationale.
3Designing for Minimisation
Build minimisation into system and process design.
Required Fields
In forms and interfaces, mark only truly necessary fields as required. Optional fields invite collection of unnecessary data.
Progressive Collection
Collect data incrementally as needs arise rather than gathering everything upfront. Ask for delivery address when shipping, not at registration.
Default Settings
Configure defaults to collect less rather than more. Users can provide additional data if they choose.
Validation Rules
Implement validation that rejects unnecessary precision. If age range suffices, do not accept or store exact birthdates.
Collection Limits
Build technical limits that prevent collection beyond necessity. If five comments suffice for feedback, do not build infrastructure for unlimited collection.
4Processing Minimisation
Limit how collected data is used and accessed.
Purpose Limitation
Use data only for the purpose for which it was collected. Do not repurpose data for new uses without fresh assessment and, where required, consent.
Access Controls
Limit access to those who need data for their function. Customer service may need different access than analytics teams.
Aggregation
Where possible, work with aggregated rather than individual-level data. Analytics often do not require identification of specific individuals.
Pseudonymisation
Where identification is not needed for processing, pseudonymise data to reduce identification risk while retaining utility.
5Retention Minimisation
Do not retain data beyond necessity.
Retention Limits
Establish and enforce retention limits based on necessity, not convenience. Delete data when the purpose is fulfilled.
Automated Deletion
Implement automated deletion processes that remove data when retention periods expire.
Archive Review
Periodically review archived data. Archives often contain data retained well beyond necessity.
Backup Lifecycle
Align backup retention with data retention policies. Backups should not preserve data that should have been deleted.
6Reviewing Existing Holdings
Apply minimisation to data already collected, not just future collection.
Data Audit
Review existing data holdings against current processing purposes. Is data being held that is no longer needed?
Legacy Cleanup
Delete data from legacy systems that served purposes no longer relevant. Migration projects are opportunities for minimisation.
Unnecessary Fields
Identify fields being collected but not used. Consider removing them from collection interfaces.
Historical Accumulation
Assess whether historical data depth is justified. Do you need ten years of transaction history or would two years suffice?
7Anonymisation and Pseudonymisation
Where data utility can be preserved without identification, consider anonymisation.
Anonymisation Assessment
Evaluate whether purposes can be served by truly anonymised data that cannot identify individuals even when combined with other information.
Pseudonymisation Application
Where full anonymisation is not feasible, pseudonymisation reduces risk while maintaining some utility for authorised purposes.
Re-identification Risk
Assess re-identification risk for anonymised or pseudonymised data. Supposedly anonymous data can sometimes be re-identified through combination with other sources.
Documentation
Document anonymisation and pseudonymisation methods used and the risk assessment supporting their adequacy.