Product
Introduction
Bayerische Versorgungskammer (BVK) is a supreme authority of the Free State of Bavaria and the largest public pension group in Germany. It jointly manages the business of twelve professional and municipal pension funds, which provide retirement, disability and survivors' benefits amounting to approximately € 4.1 billion annually on behalf of their members and insureds.
Challenge
The Bayerische Versorgungskammer (BVK) is also subject to the requirements of the GDPR and related regulations. During the introduction of a data warehouse solution and a BI tool, the need for anonymized data on the test and development system became particularly clear, since sensitive personal employee data, such as salary data, was to be stored and processed there.
Solution
In its search for a suitable solution, the Bayerische Versorgungskammer turned directly to Libelle AG. The decisive factor was that Libelle DataMasking is able to generate anonymized test data from productive data, which corresponds to the productive data in terms of structure, data field length, hierarchies and links, as well as in its description, while taking into account the special features of the data structures of the user companies - in contrast to fictitious test data. This met the requirements perfectly.
Customer feedback
Libelle DataMasking ensured a quick start into the data warehouse and BI world. A major advantage is the templates that developers can use to quickly and easily prepare their data structures for data masking. With the anonymized data, all complex issues could be tested and it was ensured that no use case that actually occurs at BVK would be forgotten.
Insight
The cooperation between the Bayerische Versorgungskammer and Libelle AG ran smoothly. Libelle contributed many ideas to the project. One challenge for Libelle was that they did not have access to the original data. These challenges were solved together with the business department. Due to the successful implementation of the data warehouse/BI tool project, Libelle DataMasking was also nominated for further applications.