Test Data Management Model –  Case Study for an EU Bank

Test Data Management Model – Case Study for an EU Bank

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
Restricted Access Offshore AccessSteps for Restricted Offshore Access

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

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