Data anonymization in companies and the challenges for such projects

AuthorMiroslav Jakovljevic
Anonymization-Icon in front of a server

Cybercrime, GDPR and data anonymization. These issues are also becoming more relevant in non-production systems. And software tests should be as valid as possible even with anonymized data. Finding a suitable solution for this is complex. The complexity of the topic almost causes many projects to fail. Our expert shows you his three biggest challenges in projects involving data anonymization in non-productive systems and explains how you can successfully master them.

Challenge 1: Stakeholders underestimate the issue of data anonymization

One project, many stakeholders, different views: During my Libelle career, there have been several stakeholders whose efforts at data anonymization were fraught with problems up front. For example, stakeholders disagreed on what the issue of data anonymization in non-production systems was all about. While it was important to some that addresses were still logically correct after anonymization, others set a completely different priority. So define clear goals with everyone involved in advance of a data protection project and ensure a uniform level of knowledge.

Challenge 2: Overly complex project management

Do you want to successfully master your data protection project? Our recommendation: Take a pragmatic approach. Define one (!) person responsible for the project and identify only the systems that are really relevant for data anonymization. Divide the personal data to be protected into profiles (names, addresses, bank data, etc.) for a structured overview. In times of Big Data and increasingly complex IT, it is elementary to have an approach that is as simple and structured as possible.

Challenge 3: A non-iterative implementation

Once you have decided on a vendor for data anonymization in non-production systems, it is essential to take an iterative approach to implementation. The project is not complete with the final decision. Every system landscape is different. Iterative validation of the test systems, anonymized data, etc. is therefore absolutely necessary for the success of the project.

Are you also considering optimizing data anonymization in your non-proactive systems? If so, our experts Miroslav Jakovljevic and Michael Schwenk will be happy to assist you at any time. Feel free to contact them for individual advice. For optimized data anonymization and the best possible project success – Libelle DataMasking.

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