Module
Module 4 of 6Lesson 2 of 2~17 min

Collect by the rules, consent, CNIL and minimisation

Choosing an analytics tool and writing a tracking plan both bear on your product's GDPR compliance. This lesson helps a PM know when consent is required, in which narrow cases the CNIL allows audience measurement without it, and which data to leave out or delete in time. You reach your DPO with a file ready for discussion.

Take this lesson: your first course is freeSee the full syllabus

Lesson objective

By the end of this lesson, you will be able to say, for each tool and each event in a tracking plan, whether it needs the user's consent or may fall under the audience measurement exemption, remove unnecessary personal data and set a retention period, before validating everything with your DPO.

Topics covered

  • consent
  • GDPR and analytics
  • audience measurement exemption
  • CNIL
  • data minimization
  • data retention

Where it fits

Reliable, compliant data

How do I know my numbers are right, and that I collect them by the rules?

Lessons in this module

  1. QA and monitor data collection
  2. Collect by the rules, consent, CNIL and minimisation (this lesson)

What you will learn in the course

This lesson is part of the course Instrument a product and use data to make decisions

  • Build a product's metrics tree (North Star, input metrics, guardrails) from the decisions to be made, and rule out vanity metrics.
  • Design an implementable tracking plan (events, properties, naming convention, identity and accounts, emitting source, owners).
  • QA and monitor event collection to get reliable data, and know what to do when it is not.
  • Collect usage data in line with the GDPR and the CNIL rules (consent, audience measurement exemption, minimisation, retention period).
  • Analyse a conversion funnel, activation and cohort retention, and interpret the resulting curves correctly.
  • Choose a product analytics tool (PostHog, Amplitude, Mixpanel, GA4, data warehouse) based on needs, constraints and cost.
  • Turn an analysis into a reasoned decision (dashboard, decision memo) while avoiding interpretation traps (vanity, Simpson, correlation, small counts).

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