In the first part of this guide, we looked at three often-overlooked cost drivers in test data management: expensive software licenses, compliance-related risks, and the cost of delayed innovation.
In this second part, we’ll dive into two more hidden cost areas:
We’ll wrap up with practical ideas you can implement right away to make your TDM more efficient and cost-effective.
Lost time: often the biggest hidden cost
Lost time due to poor test data management might seem like a soft issue, but its impact is anything but. Test teams that wait too long for usable test data, work with incomplete datasets, or manually create data, because of this valuable time and money is lost.
The rule is simple: the later an issue is found in the development process, the more time consuming and thus expensive the issue is to fix. Yet in practice, many test teams still test with unreliable or outdated test data and this can have a big impact..
The impact of poor-quality test data
Organizations that manually create test data or rely on full production copies run into several challenges:
- Increasingly complex IT environments
- Stricter privacy and compliance requirements
- Country-specific tax or regulatory rules that are hard to simulate manually
In highly regulated industries like finance or insurance—where quality, control, and compliance are critical—poor test data causes bugs to surface late in the development cycle. This leads to costly delays, unnecessary rework, and higher overall risk.
What does lost time really cost?
You wont find lost time on the balance sheet any time soon but it shows up in different ways:
- Extended sprints: More bug fixes, delayed releases
- Additional costs: Overtime, firefighting, external support
- Missed opportunities: Less focus on innovation or value creation
Bottom line: if your test data process isn’t under control, you’re wasting time—and that hits both your budget and your time to market.
Another hidden cost: infrastructure and storage. Many organizations still run their test environments based on a classic “copy production to test” model. But in a world of Agile, DevOps, and CI/CD, that model no longer fits, in the following paragraphs i will explain why.
Many organizations still run their test environments based on a classic ”copy production to test” model.
Legacy infrastructure vs. modern methods
While modern delivery practices have evolved, the underlying infrastructure often hasn’t. The result? Full copies of production environments are still being used in development, test, and acceptance stages. This leads to:
- Massive storage use: Dozens of terabytes per environment
- Higher license fees: More data = more cost
- Slow, error-prone processes: Hard to scale or adapt
Why full production copies are usually unnecessary
Most research shows that only 10–20% of production data is relevant for testing. But many teams copy everything by default, simply because it’s easy. By narrowing your data scope to what’s actually needed, you can:
- Cut storage usage by 80–90%
- Reduce software license costs
- Speed up testing and provisioning
Moving toward flexible test data management
The future lies in small, targeted datasets—subsets designed to match each test purpose and development phase. This enables teams to work faster without compromising on data quality.
Why synthetic data (still) isn’t the answer
Synthetic test data sounds promising, but the reality is: most solutions aren’t mature enough to generate complex, business-relevant datasets. Especially in domains with intricate data dependencies, the result is often unrealistic test coverage. Until synthetic data generation matures, anonymized subsets of production data remain the most effective solution.
A real-world example
A client with 40 TB of production data used to copy everything into lower environments. By switching to smart subsetting—using just 5% of the original data—they reduced storage, licensing, and infrastructure costs significantly, without sacrificing coverage or quality.
Conclusion: your hidden TDM opportunities
Where can you start improving—today?
- Automate your provisioning: Speed up delivery and reduce wait times
- Use targeted subsets: Smaller datasets = faster, cheaper, better
- Think team-first: Deliver exactly the data each team needs, when they need it
Hidden costs are real—but they’re also fixable. Whether you’re just getting started or already have advanced tooling, there’s always room for improvement.
