Test Data Hell: AI-Driven Synthetic Data and Mocking
by Eduard Dublier, CTO Skipper Soft and Automation Expert
There is a place where 90% of automation initiatives die.
And it’s not where people think.
It’s not flaky tests. It’s not CI instability. It’s not even lack of framework maturity.
It’s test data.
I’ve worked with multiple startups and scaleups this year. In every case, when we dug deep into why automation wasn’t accelerating delivery — the real bottleneck was always the same:
“We can’t run this test because we still don’t have the data to support this scenario.”
Automation ready. Pipelines ready. Test strategy ready.
But unable to execute. Because a human first needed to prepare accounts, create records, or “fix” mocks manually.
This is Test Data Hell.
The AI Turning Point
GenAI is not interesting because it “generates test cases”. This is a trivial use case.
GenAI is powerful because it can become a dynamic test data engine.
It can:
- produce domain-accurate entities on demand
- simulate realistic data distributions
- mutate data into negative / boundary / chaos states
- generate dynamic mocks for APIs that don’t exist yet
And it can do this with privacy constraints built in.
Which means: You don’t need production data anymore to test like production.
Three categories of tools worth knowing
1) Synthetic Data Platforms (Enterprise-level)
These are designed to generate statistically realistic data at scale.
- Tonic.ai – strongest for regulated domains (finance, health, insurance)
- Mostly.ai – generative anonymization with GDPR/HIPAA focus
- Gretel.ai – ML-friendly synthetic data for structured and unstructured sets
These tools can replace the need to clone production.
2) Lightweight Data Generators
Perfect for CI integration and fast prototyping.
- Mockaroo – define schema once, generate thousands of records in seconds
- Faker.js / Python Faker – low-friction randomness for unit + integration tests
These should run as pre-run steps in pipelines.
3) GPT-Based Data Factories (next level)
This is where I see the future.
We build internal domain-aware GPT agents for clients that:
- understand the business objects
- generate test entity sets by English prompts
- produce mocks for future service behavior
- automatically mutate edge cases and failures
Example prompt:
“Generate 10 invalid loan applications that trigger risk scoring exceptions.”
One command. Data ready.
Why this matters (real lesson from the field)
When test data becomes self-service and self-provisioning:
Automation stops depending on humans.
Velocity goes up. Coverage becomes honest (not just smoke). Teams stop fighting data and start testing real behavior.
This is where modern QA maturity is born.
Final thought
The fastest way today to improve delivery speed is not to rewrite the framework.
The fastest way is to automate the test data lifecycle.
Because once you get out of Test Data Hell — automation becomes a true engineering driver.
At Skipper Soft we help startups build testing ecosystems that eliminate the “data bottleneck” entirely.
If you want real acceleration — start with synthetic data and AI-driven mocking.
Everything else becomes easier after this point.
