Answer.

Managing test data is one of the oldest challenges in automation. Even at the start of automation (test scripts in Excel, macros, old QTP), data was often "stored in the head" of the test author or directly in the code. With the development of CI/CD and parallel executions, new strategies were needed: how to avoid races when multiple tests use the same data simultaneously and ensure the repeatability of results?

Problem: shared test data quickly lead to collisions and unpredictable results. Tests become unstable, are difficult to debug, data fragments "clutter" databases, and running in multiple threads leads to errors (data race).

Solution — implementing "test data per test" strategies:

Using unique or parameterized test data for each test
Generating/cleaning data before and after the test (setup/teardown, fixtures)
Separating test environments using namespaces, tenants, users
Using in-memory databases with reinitialization per test

Key features:

Generating unique data (UUID, timestamp), automatic deletion after the test
Using templates and test data factories (Test Data Factories)
Working with isolated sandbox environments

Tricky Questions.

Is it okay to use production data as test data?

No! It poses risks to security, confidentiality, and leads to unpredictability of tests due to data variability.

Is it enough to use setUp and tearDown for cleaning data?

Not always. They help minimize risks, but parallel runs may collide tests if data remains global or is not unique.

Can the same test data be used in smoke and regression scenarios?

Better — no. Smoke tests should be as independent as possible, while regression tests require comprehensive data preparation; otherwise, false positives may occur.

Common Mistakes and Anti-Patterns

Using common, "magical" data that randomly fits many tests
Uncleaned data artifacts after tests
Double usage of the same entries in all tests

Real-life Example

Negative Case

The company had one common login and several "shared" users and orders used across all automated tests. Parallel runs led to tests overwriting each other's orders or changing the status of one order in multiple threads.

Pros:

Fast and easy to implement on a small number of tests
No additional infrastructure required

Cons:

Ambiguous failures, difficulties in debugging
Unable to safely run tests in parallel or in different environments

Positive Case

Test data factories were implemented: before each test run, a unique order and user were created for each scenario, which were deleted upon test completion, and the sandbox environment was reinitialized.