White Paper Best Practices

Conquering Challenges of Data Anonymization

Need data for testing a new version of your application? Oddly enough, your production data is your best choice. What other data set better represents statistical characteristics, potentially problematic international characters, or relationships between records? But the very reason it's the safest choice for discovering application problems during development is also why it can be high-risk—privacy and data security concerns arising from use of production data in a development environment can't be ignored.

Enter a well-designed data anonymization process.

This white paper discusses the reasons for and best practices of data anonymization of production data. It's a powerful approach to obtain reliable and consistent test data that provides the same use case coverage as the original production data. It's also a way to overcome security, privacy, and licensing issues.

CloverETL is a tool that allows organizations to engage in data anonymization. CloverETL can be employed to perform system-wide transformations, converting production data into a sample anonymized data set, thus preserving the semantic relationship across heterogeneous system architecture.

Read this white paper for a detailed look at:

  • The issues and constraints to be solved when designing a data anonymization approach
  • Levels of anonymization and semantic relationship ranks
  • Best practices and techniques that make data anonymization possible
  • The advantages of a CloverETL data anonymization approach