We are in the middle of acquiring the tool. We have successfully completed a PoC with the tool, and we are in the process of onboarding it for a customer.

There were two major use cases. The test data masking and test data generation part was one of the major use cases for which we tested the tool. Integration within CI/CD pipelines was another major use case.

We are protecting our personal and sensitive information, while we provide test data to our customers for their business-as-usual and project testing.

We have several development teams within our department using the solution to create test data to test the storage for all kinds of data from our company. We can't use actual data for testing because of privacy, so we create synthetic data using DATPROF. We have one department with about 20 teams mainly using the solution on virtual machines. They are all connected to one central SQL server database in which the data is generated. We are a transportation company and all the data we deal with is regarding trains.

We use it for scrambling personal data. That's the most important use case that we have. We both have SAP and non-SAP applications.

We are using a standard software solution with some custom adjustments on an IBM DB2 LUW database. 

One of our main goals with Datprof's software was to reduce the required data storage by means of subsets. A test database where we previously used a production copy required 23 TB of disk space. By subsetting to a database with the (anonymized) data of 30,000 customers, the disk space has been reduced to only 130 GB. By using the tooling, we therefore only use 0.5% of the original amount of disk space.

The solution is used for:

1. Subsetting test data from/to databases with an incomplete relational model. (With homemade tooling it wasn't possible.)

2. Higher management demanded testing to be done with masked data

Cost reduction and time-to-market targets were the starting points of our implementation.

Subsetting an anonymous, but large production database into a manageable size, thereby reduced storage costs for our test environments. 

By shrinking the database to an easily portable database, we can refresh test databases swiftly for regression testing purposes.

For our customers, it is also a way to start the transition towards a flexible cloud/container-based test environment to meet our CI/CD targets in the future.    

We are using DATPROF Privacy and DATPROF Runtime for masking sensitive test and development data on Oracle, SQLServer, MySQL, and Postgres databases. The main purpose for using this solution is to be compliant with GDPR (General Data Protection Regulation) and for protecting sensitive business information.