AI
Test data management
AI
Synthetic data generation
The case for caution: evaluating AI-generated synthetic test data
Bert Nienhuis
Evaluating AI-Generated Synthetic Test Data | DATPROF
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As Chief of Product, I constantly evaluate emerging technologies to determine their relevance and potential impact on our platform. Generative AI is no exception, and I find myself asking: What can AI-generated synthetic test data do for us? And what are its limitations?
Let’s explore these questions. Below, you’ll find my blogpost broken down by topic:
What can AI-generated synthetic test data do for us? When to embrace new technology?
There’s no one-size-fits-all answer for deciding when to adopt new technology. Like most product professionals, I’m intrigued by innovations in AI and their potential applications. However, experience has taught me that new technologies often take years before their true value—and limitations—become clear.
AI-generated synthetic test data is a promising development. While I’m optimistic about its potential, I remain cautious, focusing on whether it genuinely enhances our platform and adds tangible value for users.
New technology is often embraced quickly in order to find a problem that it can solve. As DATPROF we start with the problem and then choose the best technology to solve the issue. From what I have seen, Gen AI is incredible powerful in creating content that doesn’t require to be perfect. Generating text, images or videos in the creative world doesn’t have the same consequence and impact as going live with a new version of a banking system to thousands of clients. Ensuring you have the proper test data is essential for validating critical applications and minimizing the risk of issues once they go live in production.
Another thing to consider; new technology is often promising, but not yet scalable. I’ve seen too many AI synthetic data generation solutions that might work on one or two tables, but fail to scale when it comes to supplying test data for an entire ERP system or multiple systems consistently.
The golden rule: why good, representative test data matters
Effective testing relies on high-quality, realistic, and representative test data. A compact and versatile dataset should be sufficient to identify all application errors, minimizing unnecessary data while maximizing accuracy.
You can run as many tests as you like, but it doesn’t matter unless your data is right
While synthetic data, including AI-generated synthetic data, can create valid test datasets, it introduces concerns about trustworthiness. Testers need confidence that their data accurately reflects real-world scenarios without introducing errors or uncertainties. When using AI-generated data, it’s vital to understand how the data was created and whether it meets your quality standards.
Why production data might still be a better choice than AI-generated test data
Using production data for testing isn’t ideal—it can raise compliance risks under GDPR and CCPA and may lack efficiency.
However, in some cases, production data offers an advantage: transparency. Since this data has already been processed by the system under test, it’s easier to rule out test data issues when debugging.
In contrast, AI-generated synthetic data may produce scenarios that are theoretically possible but highly unlikely in practice. Or worse, produce test data that looks realistic but cannot occur in production. Without a clear, auditable process to validate this data, testers could waste valuable time chasing phantom bugs caused by unrealistic test cases.
Proceed with caution: advice on using AI-generated synthetic test data
Here’s my main takeaway: be cautious with AI-generated synthetic test data.
Imagine that you have a 50 terabyte Oracle database with approximately 1000 tables, approximately 20,0000 attributes, then the connections between the data are not always clear
While it has the potential to generate realistic-looking datasets, the process is rarely straightforward. Complex models trained on production data may create outputs that mimic real data without being transparent or fully reliable. For example, validating why certain data points were generated—or why others were omitted—can be a daunting task, especially for large, intricate datasets. Imagine trying to generate synthetic data for a large Oracle database with over 1,000 tables and 20,000 attributes. Without clear documentation of the AI’s decision-making process, you risk undermining the reliability of your test environment.
Want to dive deeper into this topic? Read our more indepth article on generative AI for test data generation
Frequently Asked Questions
1. What is AI-generated synthetic test data?
AI-generated synthetic test data is artificially created test data generated by AI models. It is designed to resemble real data without directly copying production records, making it attractive for teams that want realistic test data while reducing exposure of sensitive information.
2. Is AI-generated synthetic test data always better than production data?
No. AI-generated synthetic test data can be useful, but it is not automatically better than production-like data. Teams still need to validate whether the generated data reflects real business rules, edge cases, relationships and historical patterns that matter for testing.
3. What are the risks of AI-generated synthetic test data?
The main risks are poor representativeness, hidden bias, lack of transparency, weak explainability, limited scalability and test data that looks realistic but does not trigger the same application behavior as real-world data. This can create false confidence in test results.
4. Why is representative test data important?
Representative test data is important because tests only provide value when the data reflects the scenarios the application must handle in production. If generated data misses important variations, relationships or edge cases, defects may remain undetected until after release.
5. Can AI-generated synthetic data scale to complex enterprise systems?
In most cases it won't. AI-generated synthetic data may work well for simple datasets or isolated tables, but complex enterprise systems often include hundreds or thousands of related tables, applications and data dependencies. In those cases, scalability and consistency become major challenges.
6. When should teams use AI-generated synthetic test data?
Teams should use AI-generated synthetic test data when the use case is well understood, the data can be validated, and the generated output improves testing without introducing uncertainty. It is especially useful for creating new scenarios, supplementing limited datasets or testing early-stage functionality.
7. How does DATPROF approach synthetic test data?
DATPROF takes a pragmatic approach: start with the test data problem, then choose the right technique. Depending on the situation, that may be anonymized production-like data, subsetting, deterministic generation, synthetic data generation or a combination of methods.