As software development cycles continue to accelerate, having a robust test data management strategy is no longer a nice-to-have—it’s a must-have. However, real-world data presents challenges, from privacy risks and compliance concerns to availability and consistency issues. As a result, demand for synthetic test data is growing, with Gartner research predicting it could fully overtake “real” data in AI models by 2030.
Industries such as healthcare, pharma, and finance require high-quality test data for accurate software development and testing. Using real customer data is a risk that these companies simply cannot afford, as the consequences of exposing sensitive personally identifiable information (PII) can be staggering. Beyond regulatory compliance, the benefits of synthetic test data generation include scalability, efficiency, and tangible ROI.
In this guide, we’ll explore different advanced methods for synthetic data generation, key workflow integrations, and best practices for implementation.
Understanding synthetic test data
Traditional approaches to test data management often rely on production data extracted from live environments. While this can provide accurate insights, real-world data comes with a set of issues including:
- Privacy and compliance risks – Using real data carries the risk of exposing sensitive information and violating data protection regulations
- Limited availability – Access to real data is often restricted due to security and compliance concerns, delaying testing cycles
- Data inconsistency – Extracted datasets may not cover all edge cases, leading to gaps in test coverage
- Cost and resource constraints – Maintaining and securing real test data can be expensive and require significant operational overhead
As a result of these significant challenges with real-world data accessibility (60%), complexity (57%), and availability (51%), organizations are increasingly turning to AI-generated synthetic data.
Synthetic test data is artificially generated data that maintains the statistical integrity of real-world data without exposing sensitive information. It can be created based on real-world datasets, similar to data anonymization or masking, or it can also be created from scratch to generate net new data with zero ties to real-world individuals. In both cases, the approaches used for synthetic data generation can rely on algorithms, machine learning models, or rule-based logic.
Synthetic data mitigates privacy concerns entirely while still providing a rich testing environment with improved model efficiency. It can be tailored to cover a wide range of scenarios, including rare but high-impact events that might never appear in production logs. It’s also scalable, making it easier to test for future growth.
Benefits of synthetic test data:
- Enhanced privacy & regulatory compliance: Eliminates the risks associated with using potentially sensitive data
- Scalability: Easily generates large datasets without manual data collection
- Edge-case testing: Allows for testing rare and complex scenarios that may not exist in real-world data
That said, creating high-quality synthetic data isn’t simple. It requires deep domain expertise and smart tooling. Predicting future scenarios or rare events also demands careful analysis, often best handled with intelligent algorithms.
Rule-based vs. model-based synthetic test data generation
There are two primary approaches to synthetic test data generation: rule-based and model-based (statistical) synthesis. Each has distinct strengths and use cases, and some solutions incorporate elements of both.
Rule-based synthetic test data generation
Rule-based synthetic data generation relies on predefined logic, constraints, and deterministic patterns to create test data. This approach to synthetic test data generation offers full control over the generated data, making it ideal for ensuring compliance, covering specific edge cases, and maintaining referential integrity across complex data structures.
Key advantages:
- Can maintain consistency across relational datasets
- Allows for precise control over constraints, formats, and edge cases
- Ensures compliance by defining strict rules that adhere to regulatory requirements including GDPR
However, rule-based generation can require manual effort to define constraints and may not capture the full statistical complexity of real-world data as effectively as model-based approaches.
Model-based (statistical) synthetic test data generation
Model-based (or statistical) synthesis uses machine learning techniques to analyze real data and generate synthetic datasets that preserve its statistical properties, such as distributions, correlations, and variability. This approach to synthetic test data generation has gained traction as a powerful way to create data that looks and behaves like real-world data without directly replicating it.
Key advantages:
- Can generate data with realistic statistical distributions
- Useful for machine learning model training and analytical testing
- Requires less manual configuration compared to rule-based methods
Despite its promise, model-based synthesis presents significant challenges at scale, particularly when working with relational databases spanning multiple tables. While single-table synthesis has seen meaningful progress, generating realistic, interconnected relational data remains an active area of research. The complexity of capturing dependencies across tables, preserving relationships, and ensuring consistency at scale makes this a difficult problem to solve.
Hybrid approaches
Some organizations combine rule-based and model-based synthesis to balance control with realism. A hybrid approach to synthetic test data generation may use statistical modeling to generate certain data types while applying rule-based transformations to fine-tune edge cases, enforce constraints, or ensure referential integrity. The next breakthroughs in synthetic data generation will likely come from innovations that tackle the complexity of multi-table relational data synthesis at scale, a challenge that remains at the forefront of industry R&D.
Unblock product innovation with high-fidelity synthetic data that mirrors your data's context and relationships.
Advanced techniques for synthetic test data generation
As the need for high-quality synthetic test data grows, traditional rule-based and statistical approaches have evolved into more advanced techniques that leverage machine learning and deep learning. These methods can generate more realistic and complex datasets, capturing intricate patterns in the underlying data. However, they also introduce challenges in terms of computational requirements and applicability, particularly when synthesizing large-scale relational databases.
Below, we explore some advanced techniques used for synthetic test data generation.
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs) represent one of the most powerful methods for creating synthetic data. A GAN consists of two neural networks (a generator and a discriminator) that work in opposition to refine the quality of the generated data. The generator attempts to create synthetic data that mimics real-world distributions, while the discriminator evaluates whether the generated data is authentic or synthetic. Over multiple iterations, this adversarial process results in synthetic datasets that closely resemble real-world data in terms of structure and statistical properties.
GANs have proven particularly useful in areas such as image synthesis, financial fraud detection, and medical research, where realistic yet privacy-preserving datasets are required. However, applying GANs to structured test data remains an ongoing challenge. While GANs excel at generating synthetic data for single-table datasets, they struggle to maintain referential integrity across multiple tables, making them impractical for large-scale enterprise applications that require consistent, structured data.
Variational Autoencoders (VAEs)
Variational Autoencoders (VAEs) take a different approach to synthetic test data generation by leveraging probabilistic modeling. A VAE encodes real data into a lower-dimensional latent space, applies transformations, and then decodes it back into synthetic data. This approach helps VAEs capture complex data distributions while allowing for some randomness, which can enhance the diversity of generated datasets.
VAEs are particularly effective for continuous data generation and have been widely used in domains such as medical research, finance, and security testing, where generating statistically valid yet anonymized datasets is critical. Like GANs, however, VAEs currently face difficulties when it comes to generating relational datasets at scale. They are excellent at preserving statistical properties within a single table but have limited capability to enforce cross-table relationships and dependencies, which are essential for database integrity in software testing scenarios.
Advanced statistical modeling techniques
Techniques such as Bayesian networks and Monte Carlo simulations help capture dependencies and probabilities within data, ensuring a diverse yet statistically accurate dataset. Beyond deep learning, a range of statistical modeling techniques are used to generate synthetic data with a focus on realism and privacy preservation. These include:
- Random sampling: A fundamental technique where values are randomly drawn from predefined probability distributions to create synthetic datasets. While simple, this method does not capture complex relationships between data points.
- Conditional data generation: A rule-based method that generates data based on predefined constraints, ensuring that synthetic data adheres to business rules and domain-specific logic. This approach is effective for structured test data but lacks adaptability for more dynamic datasets.
- Data augmentation: A technique commonly used in machine learning, data augmentation involves modifying existing data (e.g., adding noise, transformations) to create new variations while preserving key attributes. This approach is especially useful for text and image datasets.
While these techniques provide foundational approaches to synthetic test data generation, they often need to be combined to achieve both control and realism. No single method is universally effective, and most organizations find that a hybrid approach yields the best results.
Overcoming challenges in synthetic test data generation
A recent survey of DevOps professionals showed that 78% rely on AI as a core element of software development. But while synthetic test data presents a powerful solution for software development and testing, generating high-quality, privacy-preserving, and statistically accurate data comes with its challenges. To mitigate these risks, organizations must adopt best practices that ensure the privacy, security, and utility of their synthetic datasets.
Ensuring data privacy and security
Protecting privacy in synthetic data generation requires understanding the original data to maintain statistical accuracy while preventing re-identification. Techniques like differential privacy help obscure individual records but may also remove critical edge cases. Compliance with regulations (e.g., GDPR, HIPAA) is essential, along with thorough documentation, ongoing monitoring, and risk assessments to mitigate data exposure. Statistical tests, privacy audits, and compliance checks should be performed to ensure that synthetic data does not inadvertently expose patterns from real-world datasets.
Maintaining data utility and accuracy
Even when privacy concerns are mitigated, synthetic data must still serve its intended purpose of providing a reliable test environment that mirrors real-world conditions. Synthetic data must balance realism with usability by preserving key statistical properties and logical relationships. Validation against real-world datasets ensures accuracy, while automated quality checks help detect anomalies or biases.
Diversity and representativeness
Ensuring synthetic test data reflects real-world diversity is key to effective software development and AI training. Incorporate edge cases and rare occurrences to improve system robustness while maintaining balanced distributions through statistical analysis. By proactively modeling diverse user scenarios, synthetic data helps mitigate bias and ensures that applications perform reliably across varied inputs and conditions.
Addressing data quality
The effectiveness of synthetic test data hinges on its quality. Poorly generated data can lead to misleading test results, missed edge cases, or inconsistencies that compromise software development. To ensure synthetic data remains useful and reliable, teams should validate against real-world benchmarks, preserve logical relationships, and monitor for unintended biases.
Integration with existing workflows
For synthetic test data to be effective, it must integrate seamlessly with existing software development and testing workflows, ensuring that teams can access high-quality, privacy-safe data without disruption.
- CI/CD Integration: Synthetic data should be automatically generated and refreshed within cotinuous integration and continuous deployment (CI/CD) pipelines. By embedding data synthesis into automated testing workflows, teams can ensure their test environments always reflect the latest, most relevant data without manual effort.
- APIs for Data Generation: Solutions like Tonic.ai provide APIs that allow development and testing teams to generate and retrieve synthetic data programmatically. This flexibility enables on-demand data provisioning tailored to specific test scenarios, reducing delays in the development lifecycle.
- Scalability & Automation: Cloud-based synthetic data platforms eliminate bottlenecks by streamlining database management and data provisioning. Automated workflows ensure that synthetic data keeps pace with evolving application requirements, supporting everything from local development to enterprise-wide testing environments.
Create synthetic test data with Tonic.ai
Synthetic test data generation allows businesses to create scalable, diverse, and privacy-compliant datasets that drive more effective software testing. As the field continues to evolve, advancements in multi-table synthesis, privacy-preserving AI, and scalable data generation techniques will further enhance the usability of synthetic data. Organizations that stay ahead of these developments will be well-positioned to leverage synthetic test data as a competitive advantage, enabling more efficient and secure software testing in an increasingly data-driven world.
Whether you’re developing software, training AI models, or ensuring compliance, Tonic.ai empowers teams with the most realistic, privacy-preserving synthetic data available. Tonic Structural simplifies the generation of realistic test data from structured and semi-structured sources, offering PII detection, de-identification, synthesis, and subsetting to provide developers with high-fidelity, production-like data. Tonic Textual enables AI teams to safely leverage unstructured free-text data by using proprietary Named Entity Recognition (NER) models to redact or replace sensitive entities. It transforms raw text into AI-friendly formats, optimizing ingestion, vectorization, and training for LLMs and enterprise RAG systems.
Book a demo today to discover how Tonic.ai’s synthetic data can free your team from delays and risks, while delivering scale, speed, and safety.
FAQs
Rule-based synthetic data generation follows predefined rules to create structured, consistent data, making it useful for well-defined formats but requiring manual maintenance. Model-based generation, using statistical and machine learning techniques, learns patterns from real data to produce realistic synthetic versions. While model-based approaches improve realism, they can be computationally intensive and struggle with relational datasets at scale. Both methods have strengths, and choosing the right approach depends on the specific use case.
Advanced techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) generate realistic synthetic data by learning from existing datasets. While these deep learning models work well for single-table data, they face challenges in synthesizing multi-table relational databases. Hybrid approaches combining multiple techniques are often used to balance realism, scalability, and control.
Synthetic data supports software testing, AI model training, and privacy-compliant analytics across industries. In finance, it aids in fraud detection and risk modeling, while healthcare organizations use it to train AI on de-identified patient data. Retail, cybersecurity, and automotive sectors leverage it for simulations, anomaly detection, and predictive modeling. By enabling safe and scalable data use, synthetic data enhances innovation while protecting sensitive information.
Ensuring privacy is a challenge, as poorly generated synthetic data can still expose sensitive patterns. Maintaining data utility requires synthetic datasets to preserve real-world statistical properties without introducing biases. Scaling synthetic data for relational databases remains difficult, as current model-based methods struggle with cross-table dependencies. Continuous validation and refinement are necessary to ensure synthetic data remains accurate and effective.
Understanding the source data’s structure and dependencies helps ensure realistic synthetic outputs. Privacy techniques like differential privacy can protect sensitive information while maintaining data utility. Regular validation against real-world scenarios is essential to confirm data quality. Automating synthetic data generation through CI/CD integration and APIs improves scalability and accessibility for development teams.
A bilingual wordsmith dedicated to the art of engineering with words, Chiara has over a decade of experience supporting corporate communications at multi-national companies. She once translated for the Pope; it has more overlap with translating for developers than you might think.
