How to gather test data for testing purposes: a guide

Whether you're evaluating the functionality of a new feature, checking how your app performs under load, or testing for vulnerabilities before launch, high-quality test data is non-negotiable. So how do you collect, create, or synthesize that data efficiently—and securely?

In this guide, we break down how to gather test data for testing purposes, the different sources and techniques available, the challenges you may face, and best practices for aligning your test data strategy with compliance and scale.

Understanding test data & its role

What is test data? Test data refers to the input data used to validate software functionality, performance, and security. It mimics real-world scenarios to ensure your application behaves as expected under various conditions, from typical usage to edge cases.

Without accurate and relevant test data, even the most well-written test cases can fail to identify critical issues. Test data allows teams to:

• Simulate user behaviors
• Trigger specific application responses
• Stress-test systems under load
• Validate data flows and integrations
• Uncover bugs and security vulnerabilities

In modern development, test data must be both realistic and privacy-safe. This is especially important for industries like healthcare, finance, and insurance, where production data includes regulated information. Poor-quality or outdated test data can lead to missed bugs, performance issues, or even compliance violations. That’s why test data management is now considered a foundational part of the QA process, not just a supporting task.

How to gather test data for testing purposes

Different testing scenarios call for different approaches to sourcing data. Below are the most common—and effective—methods for gathering test data for testing purposes, alongside the tradeoffs of each approach.

1. Use production data (when data isn’t sensitive or regulated)

In some environments, production data is the simplest and most accurate choice for test data. It provides high fidelity and ensures that test cases reflect actual user behavior, making it ideal for debugging real-world issues or conducting regression testing.

However, using production data directly comes with major risks, particularly if it includes personally identifiable information (PII) or protected health information (PHI). Regulations like HIPAA, GDPR, and CCPA strictly govern the use of such data. Unless your production data is free of sensitive fields and regulated datatypes, this approach should be avoided in favor of safer alternatives.

2. Manually create dummy data

Developers and QA teams often create dummy data using internal scripts, randomization libraries, or open-source tools. This method gives you complete control over the structure and values of the data, and it can be useful for unit testing or for seeding small, targeted datasets.

But manual creation has limitations. It can be time-consuming, may not reflect real-world complexity, and often lacks the variability needed for thorough performance and edge case testing.

3. De-identify production data in-house

Some teams develop custom scripts or pipelines to mask, redact, or substitute sensitive data in production datasets. This can be a step up from manual creation, providing realistic data while reducing compliance risk.

That said, in-house redaction is difficult to scale. It can be error-prone and doesn’t always adapt well to changes in schema or new data types. Without continuous updates and monitoring, these pipelines may introduce gaps in privacy protection.

4. De-identify production data using a third-party solution

Using a tool like Tonic Structural, teams can automate the process of generating safe, de-identified test data that closely mimics production. These platforms handle complex relationships across databases, enforce compliance controls, and scale with your environment.

Tonic.ai helps teams replace manual redaction and internal scripts with automated pipelines that protect privacy while preserving data utility. For example, Patterson Dental used Tonic Structural to cut test data prep time by 75% while maintaining HIPAA compliance and improving performance testing coverage.

5. Generate synthetic data using a third-party solution

Synthetic data is emerging as a game-changer in test data strategy. By simulating production-like datasets from scratch, synthetic data avoids the privacy pitfalls of real data entirely. And with solutions  like Tonic Structural and  Tonic Textual, you can generate structured or unstructured data that maintains semantic context and business logic.

Synthetic data is especially valuable for early-stage development, testing edge cases, training machine learning models, or enabling safe access to data in off-shore environments. It’s also ideal for testing at scale without risking real customer information.

Types of test data

Different testing goals require different types of test data. To ensure full coverage, teams should work with a mix of data types that simulate real-world conditions and edge cases. Below are the primary categories to consider when creating or gathering test data:

Valid test data

This is data that meets all the input requirements and reflects expected usage scenarios. It helps verify that the application performs correctly when provided with well-formed inputs.

• Example: A correctly formatted email address in a registration form.
• Use case: Functional testing, regression testing.

Invalid test data

Invalid data helps you test how your application handles errors, exceptions, and unexpected inputs. This category includes:

• Null values – To check how the app handles missing required fields.
• Out-of-range values – For numeric or date fields that have set boundaries.
• Special characters – To identify injection risks or formatting issues.
• Invalid data formats – Like entering letters in a phone number field.

Using invalid test data is crucial for robustness and security testing.

No data

Testing with empty input fields ensures your application handles omissions gracefully. It’s particularly useful for verifying form validations and error messaging.

• Example: Submitting a form with all fields left blank.
• Use case: Negative testing, UI testing.

Boundary data

This type of test data targets the edge limits of input fields. Boundary testing ensures your application behaves correctly at the minimum and maximum allowed values.

• Example: Submitting a password with exactly 8 and 64 characters, the lower and upper boundaries for an input constraint.
• Use case: White box and functional testing.

By incorporating all of these data types into your testing process, you can significantly increase test coverage and uncover defects that might otherwise go unnoticed.

Choosing data types for different test scenarios

Different types of software testing require specific types of test data. Aligning your data choices with your test objectives helps maximize coverage, efficiency, and accuracy.

White box testing

White box testing examines the internal logic and structure of code. To be effective, it requires test data that covers all branches, loops, and conditions in the code.

• Use valid, boundary, and invalid inputs to trigger all code paths.
• Structured datasets and code analysis tools can help dynamically generate data for comprehensive coverage.

Performance testing

Performance testing measures how a system behaves under stress or high usage. It needs large, variable datasets to emulate real-world traffic and scale.

• Use simulated production data or de-identified data that mirrors peak usage patterns.
• Include complex, relational data to assess database and API performance under load.

Security testing

Security testing evaluates a system’s ability to protect data and resist malicious attacks. It requires intentionally malformed, unauthorized, or suspicious data inputs.

• Include test cases for injection attacks, authorization bypasses, and role-based access validation.
• Use randomized or fuzzed test data to identify vulnerabilities.

Black box testing

Black box testing validates an application from an end-user perspective. It needs comprehensive input-output pairings but doesn’t rely on knowing the internal code.

• Use valid, invalid, and boundary data to simulate user interactions.
• Include localized and accessibility-friendly data to ensure coverage across demographics.

When used across testing scenarios, modern test data platforms like those offered by Tonic.ai allow teams to generate high-quality, de-identified, and production-like datasets that meet privacy standards while enabling rigorous validation. 

General best practices for test data management

To build a modern, privacy-conscious, and efficient testing practice, it’s critical to implement proven test data management practices. Below are key best practices to guide your team:

• Data quality: Ensure that your test data maintains the statistical distribution and referential integrity of production. This is particularly important when de-identifying or masking data—poor data quality can break workflows or lead to false test results.
• Data age: Keep test data up to date with the production environment. Automated pipelines and scheduled refreshes help ensure your tests reflect the latest schema and business logic.
• Data size: Tailor your test datasets to the specific needs of each test case. Subsetting large production datasets can reduce test run times while still providing meaningful coverage.
• Data security: Always apply robust de-identification techniques to safeguard sensitive data. Leveraging platforms like Tonic Structural ensures compliance with regulations while minimizing exposure risk.
• Data provisioning: Streamline how data is delivered to developers and testers. Solutions like Tonic Ephemeral allow teams to spin up on-demand datasets and tear them down automatically, reducing clutter and security risks in lower environments.
• Data infrastructure: Integrate test data generation and provisioning into your CI/CD pipelines. This automation reduces manual work and ensures consistency across teams and environments.

By adopting these best practices, you’ll improve testing efficiency, mitigate risk, and empower your development teams to build and release higher-quality software faster.

Conclusion

Gathering high-quality, production-like test data doesn’t have to be time-consuming or risky. By combining smart strategies with modern solutions, you can create scalable, privacy-safe workflows that empower developers and testers alike.

Whether you’re working with real production data, generating synthetic data, or using de-identification to preserve privacy, the key is aligning your test data management with your development goals. And with platforms like Tonic.ai, it's easier than ever to create the test data you need—when and where you need it.

Ready to take your test data strategy to the next level? Book a demo with Tonic.ai today.