There are variants and plenty of masking techniques, like using the dictionary. It was the most used technique.

Moreover, it helps with the test cycle time because without mask data, for example, we can't upload them to the cloud for the first POC for the cloud providers. So, without masking, we can't send them.

The ability to mask the same data consistently and in a repeatable way across heterogeneous data sources (e.g., Oracle, XML). This is done without the need to store original and masked value pairs anywhere physically.

Data Masking and creating subsets of data.

Informatica Test Data Management is a complete solution. I'm not very deep in the technical aspect of the solution, but most of the workflows are now completely automated. There are some minor elements where we have to still add manual parts to the implementation.

Dynamic data masking is a valuable feature of this product.

• It has good integration with other platform products
• Imports data domains and profiles created with other tools of the suite, such as analysts and developers

The data masking feature has been the most frequently used one. Masking of sensitive production data was my main job.

Data masking is the most valuable feature of this product.

The product provides four main features:

Data discovery to profile data and mask sensitive data.

Data subsetting to take a subset of production data from all schema tables while preserving referential integrity.

Data masking to create masking rules and mask the fields that we select.

Data generation If we don’t want to mask production data, the tool can generate data based on rules that we configure.

All of the features are strong, however my favorite is data masking. Also, it provides many built-in rules and rules that we can customize to mask all types of data. The masking engine is very strong.

TDM includes built in policy packs, and these are masking and data discovery techniques that are created for common confidential fields like name, Date of Birth, address… Such fields that you can find almost in every database in any company.

The data discovery techniques are regular expressions (RegEx) that are executed on the metadata (the column names) or the data itself to find matches to my discovery.

Example: a data discovery rule with a regular expression set to *name* will find all the fields that include “name” like First_name_EN, full_name_FR… but it will not return the field FN_EN (And I mean by this First Name English)

So if I want to discover all the fileds in my database that might include names I will miss some, since the data discovery rule with RegEx = *name* is very simple and needs to be more complex to cover all columns syntaxes.