Practice : Test Data Management

Purpose and Strategic Importance

Test Data Management (TDM) ensures that automated and manual tests are supported by realistic, reliable, and compliant data. Without high-quality test data, even the best tests can fail to provide meaningful confidence - leading to false results, regressions, or production issues.

Good TDM improves test coverage, speeds up pipeline execution, protects privacy, and helps simulate real-world scenarios. It's vital for quality engineering, secure delivery, and effective CI/CD.

Description of the Practice

  • TDM provides controlled and reusable data sets for use across testing environments.
  • Approaches include data subsetting, masking, synthetic data generation, and versioning.
  • Data is refreshed automatically and synchronised with test lifecycles (e.g. per test run or CI job).
  • Sensitive data is redacted or anonymised to ensure compliance with privacy laws like GDPR.
  • TDM supports unit, integration, E2E, and performance testing.

How to Practise It (Playbook)

1. Getting Started

  • Identify data dependencies in your tests (e.g. users, transactions, environments).
  • Decide on a TDM approach: use mock data, mask production, or generate synthetic sets.
  • Implement scripts or tools to provision, reset, and clean data per test run.
  • Store test data separately from test logic and manage versioning.

2. Scaling and Maturing

  • Automate data setup and teardown in pipelines and local environments.
  • Maintain golden datasets for regression and scenario testing.
  • Create personas and edge cases to support exploratory and accessibility testing.
  • Audit test environments to ensure no sensitive or stale data is used.
  • Use shared services or TDM platforms to provision consistent data at scale.

3. Team Behaviours to Encourage

  • Treat test data like code - version, review, and reuse it.
  • Align test data design with real-world usage patterns and business rules.
  • Collaborate with data stewards and security teams to ensure privacy compliance.
  • Make test data setup fast and repeatable for local dev and CI runs.

4. Watch Out For…

  • Tests that fail randomly due to shared or dirty test data.
  • Reliance on production data in lower environments - risking non-compliance.
  • Manual test data setup that becomes a bottleneck.
  • Static data sets that don’t reflect evolving use cases.

5. Signals of Success

  • Tests run consistently with predictable outcomes.
  • Engineers can quickly create or reset data for local and pipeline tests.
  • Sensitive data is protected across all environments.
  • TDM accelerates, not slows down, delivery pipelines.
  • Testing is more realistic, scalable, and compliant.