Anonymized data is a type of data that has been processed to remove any personally identifiable information (PII) or personal data. Such data is often used in research, analytics, and other data-driven activities, as well as for compliance with privacy regulations.

According to GDPR, anonymized data has been altered in such a way that it can’t be used to identify a specific person. Since anonymized data can’t be restored, it isn’t considered personal data under GDPR. This means it is exempt from GDPR.

Some examples of compliant data anonymization methods include:

  • Randomization:





    • Noise addition – Where personal identifiers are expressed imprecisely, for instance: height: 180 cm → height: 320 cm





    • Substitution – Where personal identifiers are shuffled within a table or replaced with random values, for instance: ZIP: 10120 → ZIP: postcode






  • Generalization:





    • Aggregation – Where personal identifiers are generalized into a range or group, for instance: age: 30 → age: 20-35






Removing any identifiable information from a dataset allows for meaningful analysis without compromising the privacy of individuals.

Examples of use cases for anonymized data include:

  • Measuring the effectiveness of marketing campaigns.
  • Analyzing the behavior of website or mobile app users.
  • Analyzing trends and patterns.

Further reading:


  • Life after GA4: Why EU organizations are going local

    When Universal Analytics was phased out in 2023, and GA4 rolled out with complexity, many European organisations were forced to rethink how they measure success. For more and more, the solution is clear: use analytics built for Europe, by Europe. Why sovereignty matters Data sovereignty isn’t just a buzzphrase. Under GDPR and the Schrems II…

  • Telehealth analytics: Optimizing virtual care experiences in a HIPAA-compliant way

    As patients increasingly turn to digital platforms for medical care, healthcare organizations must understand user behavior and tailor their responses to meet these expectations. Patients want flexible, digital-first options, while providers seek to optimize efficiency, reduce costs, and expand care to more people.