Therefore, it is easier to adapt to the GDPR, since this data does not generate as much impact for the data subjects. However, when it Business Email List comes to looking for leads for a later conversation that leads to a purchase , they are not helpful. In this scenario, it is essential to have personal data and direct access. One of the characteristics of Business Email List anonymous data is, precisely, the capacity that they cannot be reverted to personal data after a transformation process. That is, data that cannot be re-identified. Therefore, anonymous Business Email List data is different from pseudonymous data.
Pseudonymization of data consists of anonymizing the data, but with the possibility that it may become personal again later. Now, let's look at some data anonymization methods to better understand how this type of information works. anonymization Business Email List A common type of process is one that completely transforms Business Email List the data, removing links to the individual, without reversal, as we have already mentioned. The complete removal of a column Business Email List with personal information in a database, for example. Suppression Delete Business Email List uses fixed data to replace identifiable parts of a database.
Some examples are the use of asterisks or other standardized forms of data. Generalization consists of transforming specific data into Business Email List general categories to eliminate individual connections. An example of this is the transformation of information about a customer into data about a class or group (such as the classic definition of the audience). This is a good strategy, as it allows the active use of the data, without it being personal. Pseudonymization Business Email List A common method of pseudonymization is to use a parallel table to the one containing the personal data. In parallel tables, the data is anonymized, but allows a Business Email List connection to the original data via a key, for example.