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Test Data Management

The practice of constructing, managing, and delivering true-to-life test data for non-production testing purposes.

You can build a TDM strategy based on the type of data requirements in the project. This strategy can be in the form of the following:

  • SQL queries that extract data from multiple tables in the databases
  • Flat files based on mapping rules, simple modification, or desensitizing of production data or files
  • An intelligent combination of both above

Types of Test Data

  • Production test data : this one needs sanitization and masking before use
  • Synthetic test data : this one is generated by a tool or manually

Synthetic Test Data

Is data that is artificially manufactured rather than generated by actual events. It can be found in different flavors:

  • Wrong : inappropriate data to test the system's ability to handle errors
  • Boundary : data that is on the edge of the acceptable range
  • Valid : data that is within the acceptable range
  • Absent : data that is missing
  • Invalid : data that is outside the acceptable range

Comparing Test Data Types

Production Test Data

Pros

  • test data looks and behaves like real data
  • can be use to reflect complex system logic
  • provides the most comprehensive test coverage

Risks

  • Resources : requires tools may not be available
  • Security : cloning production data may be a security risk
  • Stale data : test data must be refreshed regularly to expose new bugs

Testing type by test level:

  • Unit testing : Synthetic test data should be use for lower-level testing
  • Component/Contract testing : Hybrid test data should be use for component testing
  • E2E testing : Production test data should be use for E2E testing

Key Test Data Management Features

  • Data masking
  • Data subsetting
  • Data refresh
  • Data provisioning
  • Data Quality and Coverage
  • Knowledge Management
  • Integration,Data access and Monitoring
  • Self-service portal

Test Data Management Workflow

The workflow follows these three main steps:

  • Identify test data requirements.
  • Apply test generation strategies.
  • Load, update, and clean up the test data.

Test Data Management Maturity levels

  • Level 1 : Defined
  • Level 2 : Managed
  • Level 3 : Optimized