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GDPR and free comment areas - 8 errors that could have been avoided

Excessive or illegal comments in your database can cause a bad reputation. Let's review some concerning events that could have been avoided, and how to avoid future ones.

Excessive or illegal comments in your databases can cause a media scandal. Here are 8 examples concerning customers, students, employees or auditors. They reveal the regulatory, economic and e-reputation impacts suffered by the companies concerned.

Free comments - When student registration puts a company in turmoil

In 2010, the CNIL issued a public warning to a tutoring company following the discovery in its databases of free comments :

  • judgments about teachers, parents and their students
  • excessive or even insulting opinions (examples: "smells like tobacco and cellar", "big jerk")
  • sensitive data: social security number, health data ("cancerous tumor", "had leukemia")
  • data related to offenses that some students may have committed in the past

The company responded with a terse press release and challenged the CNIL's decision before the Council of State. However, the decision was upheld. Even though the sanction remained moderate, its media fallout was particularly important, so much so that the company's stock price temporarily fell.

This example is particularly interesting because it reveals the consequences of a bad management of free comment areas:

  • a reinforced exposure to the CNIL which is particularly vigilant on this subject. A complaint from a client, a candidate or an employee union can lead the CNIL to look into your databases.
  • the presence of inappropriate or even illicit comments is a source of scandal. **The impact on your e-reputation and your brand image is even stronger since sensational examples of problematic comments can be published by journalists.
  • a real short-term economic impact (disruption of the stock market, administrative fine from the CNIL).
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Excessive free comments on a radio station's listeners

In 2016, the CNIL looked into in the comment databases relating to the listeners of a radio station These include:

  • excessive comments and insults
  • data relating to assumed racial origin ("Tunisian Jewish accent", "North African accent")
  • health data
  • data related to sexual orientation

In addition to their problematic nature, some of the open-ended comments date back to 2002. The company actively and voluntarily cooperated with the regulator, putting an end to these practices in 2017. Therefore, there was no public sanction. On the other hand, again, there was a significant media fallout from this case.

HRD - Keep an eye on your employee tracking files and your free comment areas

The management of personnel, temporary employees or trainees and recruitment campaigns can result in the recording of tracking files. These files contain very useful information but it is also an opportunity for their authors to "let loose."

These various companies stopped the practices involved and were forced to publicly apologize.

Customer relationship management can get out of hand because of free comments

Some e-commerce sites have fallen victim to bad practices. Customer relationship managers can be tempted to enter derogatory or even illegal comments in the CRM for all sorts of reasons.

  • In 2015, an e-commerce site received a public notice from the CNIL. The presence of illicit comments was highly publicized. This case was all the more publicized because the CNIL reproached the company for numerous failures regarding the GDPR compliance of its site, overall.
  • The same year, a dating site was also the subject of a public notice, notably because of comments qualifying customers ("ball", "whore")
  • In 2016, another e-commerce site was subject to a public warning by the CNIL

What about you? Have you brought your free comment areas into GDB compliance?

Monitor your free comment areas with Deep Learning

How can you control the use of free fields in your CRM, HRIS or ERP and moderate comments? You can, for example, create a dictionary of keywords that an automated tool will be able to prohibit from being entered. However, this dictionary must be continually enriched, as this method is often incomplete when faced with ambiguous and graphic expressions. Lettria has chosen a hybrid methodology that combines dictionaries of prohibited words, "pattern" detection of problematic sentences, and a classification model trained on our proprietary data. Only these combined techniques can apprehend the richness of the spoken language, and to correctly detect offensive comments, as well as sensitive personal data.


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