Privacy problems solved

Case study of an insurance institution

Case study

This case study shows how our software has helped an insurance institution, with their data privacy issues. By masking their test data, the environments became GDPR compliant quickly.

Organization

An organization who takes care of insurances

Our client, who remains anonymous in this case study, is an insurance
institution. The organization has more than 16.000 employees. In 2018, they paid out almost 20 billion euros in benefits.

Challenge

Different business domains with different methods

Just like many other organizations, this client no longer wanted to test their software with production data. Within the organization there are many different business domains where test data was handled differently. Sometimes fictitious data was used, sometimes production data, but the need for a solution was great. Previous experiments had not been successful, but with DATPROF Privacy a successful implementation has been created within the organization to mask data for testing purposes.

Solution

Anonymize in a separate environment

After a selection process with five different parties for test data anonymization, DATPROF was chosen. An important reason for choosing the DATPROF solution is the user-friendliness of the software compared to price / quality, our client said.

The anonymization of databases started expeditiously. The first databases were masked within 3 to 6 months. An important choice was for the anonymization to take place in a separate environment, to which only a limited number of employees have access. After an adjustment period, the employees appeared to be able to work fairly quickly with masked test data.

Benefits

Reusability of templates

An important advantage that was achieved relatively quickly is that many environments became GDPR compliant because copies of production were no longer used. The entire test data management process shows that the reusability of DATPROF templates is particularly valuable.

What our client intends to tackle in the future is the anonymization of chains and the minimization of test data. It has become clear that the latter can yield a considerable profit in storage and throughput times.

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