The platform's ability to generate metrics is the most valuable feature for data management. It is a user-friendly and easy-to-use tool.

We use these functions:

• Archive – archive data to PST files, we do not archive data to the archive DBMS
• Data privacy – masking of PII and PHI data
• Submitter – test data management

This product archives all types of relational databases. It is the original archive solution for business.

Even when a masking-on-demand feature was provided, we decided to write our own to meet the actual business needs. Currently embedded in an Oracle instance, it allows us to reach all kinds of RDBMS.

I really appreciate using the match keys and comparing the records before and after masking. This is done by joining multiple tables to form a single record. This helps to get a faster validation of data.

I like the features compared to other tools. This tool is more flexible to use than other tools like an Oracle product or SAP product. I think IBM tools are more flexible, and this tool is also a good product.

I value data masking, data extraction/sub-setting, data comparison, and data load.

The Data Archive or Data Growth feature is one of the very useful solutions that help organizations to manage and support their database archiving strategies. This feature also enables business policies, data retention and access control.

• Data growth solution
• Data masking and test data management
• Data decommission

• Data archiving reduces application performance issues and storage.
• Test data management
• Data masking for GDPR.

Its data masking feature is very effective and the latest improvisation in this is UMask which I expect to be another powerful add-on.

Data masking features are the most valuable, as non-production environments are used by diverse parties and data privacy has to be guaranteed.

• Data masking
• Test data environment setup and management

• Optimizing and automating recurring processes that create and manage non-production environment data.
• Creating right-sized fictionalized test databases.
• Protecting sensitive data in non-production environments: Sensitive data such as national IDs, credit card numbers, email addresses and confidential corporate information can be masked to protect it from misuse and fraud.

Data masking with consistency across databases and applications is the most valuable feature.