Use this skill when reviewing analytics platform configuration — GA4 property settings, BigQuery export schema, custom event-parameter definitions, and user-property declarations — for data-minimization violations, excessive collection, and storage-period over-retention. Trigger when a user provides a GA4 property configuration export, a BigQuery raw-event export schema, a custom event or user-property inventory, data-retention settings, or asks whether their analytics setup collects more personal data than necessary, retains data longer than required, or converts an analytics platform into a personal-data processor. Distinct from marketing-pixel-data-leakage-review: this skill reviews what analytics platforms collect and retain internally, not outbound pixel payloads to ad networks.
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name
analytics-data-minimization-review
description
Use this skill when reviewing analytics platform configuration — GA4 property settings, BigQuery export schema, custom event-parameter definitions, and user-property declarations — for data-minimization violations, excessive collection, and storage-period over-retention. Trigger when a user provides a GA4 property configuration export, a BigQuery raw-event export schema, a custom event or user-property inventory, data-retention settings, or asks whether their analytics setup collects more personal data than necessary, retains data longer than required, or converts an analytics platform into a personal-data processor. Distinct from marketing-pixel-data-leakage-review: this skill reviews what analytics platforms collect and retain internally, not outbound pixel payloads to ad networks.
Analytics Data-Minimization Review
Purpose
This skill reviews analytics platform configuration — GA4 property settings, BigQuery export schema, custom event-parameter definitions, and user-property declarations — for data-minimization violations, excessive collection, and storage-period over-retention. Analytics platforms are a primary regulatory surface for GDPR enforcement: European DPAs (Austrian DSB, French CNIL, Italian Garante) have found that user_pseudo_id, IP address, and precise geo combined with a BigQuery export constitute transfers of personal data requiring a lawful basis, a valid transfer mechanism, and compliance with the storage-limitation principle under GDPR Article 5(1)(e). This skill is distinct from marketing-pixel-data-leakage-review — it reviews what analytics platforms collect and retain internally (schema, user properties, retention periods), not outbound pixel payloads transmitted to ad networks. The review works from sanitized configuration exports only; never request live analytics data or real user identifiers.
Lean operating rules
Treat a GA4 user-scoped custom dimension populated with a persistent first-party user ID linked to a CRM contact record as HIGH — it converts GA4 into a personal-data processor for identified individuals, triggering DPA obligations and requiring a separate documented lawful basis beyond the analytics purpose.
Treat a BigQuery raw-event export retaining user_pseudo_id and geo.city at full precision with no anonymization transform or partitioned deletion job as HIGH — the combination of fields constitutes personal data under GDPR, and uncontrolled raw export creates an unmanaged data store with no retention ceiling.
Treat a data-retention period set to the maximum (14 months in GA4) with no documented justification tied to a specific, time-bound analytical purpose as HIGH — GDPR Article 5(1)(e) requires retention only as long as necessary; the maximum is not a default entitlement.
Treat user properties collecting device fingerprint components, precise IP, or persistent advertising identifiers (GCLID, FBCLID passed as user properties) in a property lacking a valid transfer mechanism for non-EEA exports as HIGH — these fields individually or in combination constitute personal data with cross-border transfer obligations.
Treat event parameters collecting free-text field values from search queries, form inputs, or support chats as HIGH — free-text fields frequently contain names, emails, or health information that exceed the analytics collection purpose.
Treat session-scoped custom dimensions collecting full URL paths that include query parameters with PII (e.g., /reset?email=user@example.com) as HIGH — URL-embedded PII is personal data regardless of whether it was intentionally collected.
Flag custom event schemas that duplicate standard GA4 automatically collected events with additional parameters adding no documented analytical value as MEDIUM — redundant collection without justification violates data minimization under GDPR Article 5(1)(c).
Flag BigQuery export schemas that retain raw event data beyond the property's configured retention period because no partition-expiry or scheduled query enforces deletion as MEDIUM — the property setting does not automatically govern the export.
Flag user-property schemas with no documented owner, purpose, or review date as MEDIUM — absence of governance documentation is a proxy indicator of speculative or abandoned collection.
Do not recommend disabling an event or parameter without naming the analytical purpose it serves and the impact of its removal on measurement continuity.
Label every finding with evidence basis: configuration export provided, schema provided, documentation-based, or inference from missing element.
