The topic of GDPR has been relevant for all companies since 2018, and data processing and storage processes would be unimaginable without it. With the GDPR law, companies were force to handle personal data and ensure their protection responsibly.
Art. 5 GDPR deals by the “Principles for processing personal data” and paragraph 1 lit c. In this article, you will find the term data minimization.
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But what does data minimization nasty?
Data minimization is one of the values in the dispensation of personal data. For this reason, the law stipulates that personal data must be “adequate and relevant to the purpose and incomplete to what is necessary for treatment.” (Source: Art. 5 para. One letter c.) RGPD)
The basic idea behind the term facts minimization is that personal data may only be collect if this is necessary for the respective purpose. The focus is on the data’s scope and the processing’s type and duration. If these points are not given, no data may be collect. (Font)
What are the profits of data minimization for businesses?
What is not calm does not have to be saved and therefore not protected or deleted! Sounds funny, but that’s how it is! – This is the obvious benefit of data minimization. This law section prevents or prohibits the “unnecessary” collection of irrelevant data. The aim is, therefore, to have only “pure” data sets without attributes that are not relevant for the data processing process, for example, Keyword data garbage.
A look at practice: Example GDPR data minimization
If we clarify this using the example of online shops, they may only collect personal data necessary for the collation process.
You can recognize this data as a user because it is a mandatory field. All other information must be provided voluntarily to the customer. If this is not the case and the data is irrelevant to the ordering process, there has been a data protection violation, and the company can be prosecuted. This is a typical example of data minimization as defined by the GDPR.
How can companies implement data minimization?
In general, you must always ask yourself whether a lot of data is collect (e.g., through surveys) and whether this questionnaire and the data collection are still GDPR-compliant. A company should continually ask itself: “Does the survey already fall under the Data Minimization Act?”.
In practice, the following points can help:
- Reduction of the attributes of the owners of the data to be collect
- Set restrictions as presets that allow the processing of personal data only for the appropriate purpose
- Suppression of data fields using a data mask
- Automated blocking, pseudonymization, and anonymization procedures and routines
- Definition and application of a deletion concept (source)
How do companies meet the challenge of GDPR-compliant data?
When collecting large quantities of data, the question always arises as to how the project can be reconcile with the standard of data minimization. Here, companies usually rely on the anonymization or pseudonymization of personal data. In this way, the data can be processe without it being possible to conclude specific persons. Therefore, personal data is protected and processed following the GDPR.
What is behind anonymization and pseudonymization are explain in more detail in another blog post. With Libelle DataMasking, the Libelle IT Group has developed a solution for the required anonymization and pseudonymization. The key generates anonymous and logically consistent data across all development, test, and QA platforms.
The anonymization procedures deliver realistic and logically correct values with which relevant business cases can be describe and meaningfully tested from start to finish. In addition, developers and users have a “clean” database with which they do not have to worry about data protection.