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.

CloverDX is a tool that allows organizations to engage in data anonymization. CloverDX 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:


Data integration software and ETL tools provided by the CloverDX platform (formerly known as CloverETL) offer solutions for data management tasks such as data integration, data migration, or data quality. CloverDX is a vital part of enterprise solutions such as data warehousing, business intelligence (BI) or master data management (MDM). CloverDX Designer (formerly known as CloverETL Designer) is a visual data transformation designer that helps define data flows and transformations in a quick, visual, and intuitive way. CloverDX Server (formerly known as CloverETL Server) is an enterprise ETL and data integration runtime environment. It offers a set of enterprise features such as automation, monitoring, user management, real-time ETL, data API services, clustering, or cloud data integration.