Defining Test Data Management Goals for the Bank:

1. Optimized effort of QA
2. Enhanced offshoring

Other Factors For Defining Test Data Management Goals for the Bank:

• No centralized process set for test data management for test execution
• Each tester navigates through the available data set to find relevant test data resulting in more effort and time
• No ownership to ensure that offshore has access only to Artificial  Data
• Huge efforts spend on identifying and creating test data for every release after the test environment rebuilt

Solution Approach

Two-Stage Test Data Creation Process

1. Anonymization of Production Data

• Data anonymization is done while maintaining referential integrity  
• Anonymization rules ensure that the business scenarios/rules for each data are unaffected  
• For e.g. age, location, account values are not disturbed, while DOB, address account numbers are anonymized
• Trivialization, use of common names, exchange A->B, Same set rules, etc.

Tools used for Anonymization

• Data Copy by DBA
• PL/SQL Stored Procedures for Oracle DB 
• Python Based Scripts
• Banks prefer not to use other industry-standard tools
  –  For security reasons
  –  As using other tools need more schema knowledge, while this method is one time copy of all data

Goals of Test data management team:

• To provide timely test data support to all the testing groups 
• Restore the system (Test Data) to its original state after every Test environment re-built 
• Create more artificial test data to improve the offshoring ratio
• To ensure offshore has access to ONLY artificial data
• Define processes that will help maintain huge sets of test data 
• Create test data for new modules and maintain an existing test data set

2. Creation of Artificial Data

• Selective test data created by QA team in UAT env and copied to QA env
• Completely artificial users created covering all key scenarios
• Application-specific data creation done
• Requires more knowledge of schema, workflows, and     business rules
• This data is accessible for offshore testing

Tools used for artificial data creation

• Created directly via UI through simple automated scripts in JAVA/Selenium
• Standard industry tools can also be used depending on the tech stack and tools used in the org.

Data Optimization and Enrichment

• Maintaining and retrieving the created data efficiently is a challenge
• The dynamic nature of multiple applications requires different test data
• Optimizing common data user requirements across applications to optimize and eliminate excess artificial users is done
• Every new requirement verified against existing data set to reuse with modification of rights and roles as needed