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Part 2: Identifying and reducing the costs of your test data processes
Maarten Urbach
Part 2: Reducing the costs of your TDM process
9:04
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 Yet in practice, many test teams still test with unreliable or outdated test data and this can have a big impact.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.
Infrastructure and storage: a silent killer
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.
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
Already using a TDM tool? Great—but chances are, there’s still a lot of untapped potential. Many organizations stop at data masking for compliance, while other major cost savings remain untouched.
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.
Frequently Asked Questions
1. What are the hidden costs of test data management?
Hidden test data management costs often include waiting time, manual data preparation, delayed releases, infrastructure costs, storage costs, software license costs and the cost of fixing defects late in the development process.
2. Why does poor test data management waste time?
Poor test data management wastes time because testers and developers may wait for usable data, work with incomplete datasets or manually create data for specific scenarios. This slows down sprints and can cause defects to be found later, when they are more expensive to fix.
3. How do full production copies increase TDM costs?
Full production copies increase costs because they require more storage, more infrastructure and often higher software license costs. They also take longer to refresh and are harder to manage in Agile, DevOps and CI/CD environments.
4. How can data subsetting reduce test data costs?
Data subsetting reduces costs by selecting only the data needed for a specific test purpose or development phase. Smaller datasets are faster to provision, cheaper to store and easier to refresh, while still preserving relevant test coverage.
5. Is synthetic data always the best way to reduce TDM costs?
No. Synthetic data can be useful, but it will not capture complex business rules, dependencies and edge cases in mature enterprise systems. In many situations, anonymized subsets of production-like data provide a better balance between realism, cost and control.
6. How can automation reduce the cost of test data management?
Automation reduces TDM costs by removing manual handovers, speeding up provisioning and making test data refreshes repeatable. Automated workflows help teams get the right data faster while reducing operational effort and errors.
7. How does DATPROF help reduce TDM costs?
DATPROF helps reduce TDM costs by combining data anonymization, subsetting, automation and controlled test data provisioning. This helps teams avoid unnecessary full production copies, reduce storage usage, speed up delivery and improve test data quality.