What is Synthetic Data?

Synthetic data is generated artificially using algorithms and computer simulations in order to mimic the statistical properties of real-world data. This type of data can be fully synthetic, where no real data elements are used, or partially synthetic, which involves blending synthetic elements with real data points to enhance privacy or fill gaps.

Fully synthetic datasets are entirely artificial, containing no real data elements, whereas partially synthetic datasets replace only sensitive data elements with synthetic equivalents, enhancing privacy and data utility without compromising compliance.

Why is Synthetic Data Important?

With the growing emphasis on data privacy and the complexities of data governance, synthetic data offers several compelling advantages over using real-world data:

Enhanced Data Privacy

By utilizing synthetic data, organizations can minimize the risk of exposing personally identifiable information (PII) during data analysis and model development. This is particularly crucial for industries that handle sensitive data, such as healthcare, finance, and retail.

Improved Model Training and Testing

Synthetic data can be readily generated in large volumes, allowing for the creation of robust and diverse datasets for training and testing machine learning models. Real-world data sets might be limited in size or scope, potentially leading to biased or inaccurate models. Synthetic data generation can address these limitations by creating datasets that encompass a wider range of scenarios and data points.

What is Synthetic Data Used For?

The applications of synthetic data are broad, ranging from training AI models to enhancing data security. It’s particularly useful in industries where data sensitivity or scarcity is a concern.

Training Machine Learning Models

Synthetic data is crucial for training machine learning models, especially where real data is scarce, sensitive, or expensive to collect. It facilitates the creation of large, diverse datasets necessary for algorithms to learn complex patterns.

Enhancing Data Security and Testing

Synthetic data allows organizations to test systems, develop products, and conduct research without risking sensitive or proprietary data exposure, securing intellectual property and ensuring data integrity.

Challenges and Limitations of Synthetic Data

While synthetic data is a powerful tool for enhancing data privacy and expanding training datasets without compromising real-world data, it comes with its own set of challenges and limitations that need careful consideration.

Lack of Realism and Accuracy

Synthetic data struggles to capture the complex nuances of real-world data fully. Although it replicates patterns and correlations found in real data, its generation models may not always accurately reflect the true distributions, potentially leading to less effective training outcomes. This is especially true in complex scenarios like natural language processing or high-resolution image generation where capturing subtleties is crucial .

Complexity in Generation

The generation of synthetic data for complex data types like text and images requires advanced models such as Generative Adversarial Networks (GANs) or Variational Auto-encoders (VAEs). These technologies are sophisticated and require substantial computational resources and expertise, which can be a barrier for some organizations​.

Validation Challenges

Ensuring that synthetic data accurately reflects real-world conditions is a major challenge. Validating the quality and usefulness of synthetic data involves comparing it against real data to ensure it maintains integrity without introducing significant biases or errors​.

Best Practices for Implementing Synthetic Data

To effectively implement synthetic data within your operations, several best practices should be followed to maximize its benefits while mitigating potential drawbacks.

1. Ensure Data Diversity and Quality

   1. It is crucial to ensure that synthetic data covers a diverse range of scenarios and variables that might occur in the real world. This helps in minimizing bias and improving the robustness of the models trained on this data​.

2. Use Advanced Modeling Techniques

   1. Employing the latest in generative modeling technology can help in creating more realistic and valuable synthetic datasets. Techniques like deep learning methods, specifically GANs and VAEs, have proven effective in generating high-quality synthetic data that closely mimics real data characteristics​.

3. Regular Validation Against Real Data

   1. Continuously validating synthetic data against real-world data is essential for ensuring that it remains relevant and effective. This involves statistical analysis and testing to verify that synthetic data maintains a high level of accuracy and reliability​.

Stop relying on confidential production data. Masked data may not be realistic or robust enough either. IRI RowGen allows for the creation of realistic synthetic data that can be used in place of sensitive real data, ensuring compliance with data privacy regulations. This tool is designed to generate large volumes of data quickly and efficiently, mimicking the complexity and variety of real datasets without compromising privacy

IRI RowGen uses your metadata and business rules to make better test data. RowGen automatically builds and rapidly populates massive DB, file, and report targets with pre-sorted, structurally and referentially correct test data.

For more detailed information on how RowGen can help you create and leverage synthetic test data, visit iri.com/rowgen.

Frequently Asked Questions (FAQs)

1. What is synthetic data?

Synthetic data is artificially generated data that mimics the statistical patterns and structure of real-world data. It is created using algorithms or simulations and can be fully synthetic—containing no real values—or partially synthetic, where only specific fields are replaced to enhance privacy.

2. How is synthetic data different from masked data?

While masked data originates from real datasets and has sensitive values obscured, synthetic data is generated from scratch or modeled using statistical patterns. Masked data may preserve some original risks if poorly masked, whereas synthetic data can provide complete privacy when properly generated.

3. What is synthetic data used for?

Synthetic data is used for training machine learning models, testing software, developing analytics pipelines, sharing data safely with third parties, and enhancing privacy during development or research without exposing real data subjects.

4. How does synthetic data improve machine learning model training?

Synthetic data allows teams to generate large, diverse, and balanced datasets that may be difficult or expensive to gather from real-world sources. This improves training coverage, reduces bias, and accelerates experimentation in AI development.

5. What are the benefits of using synthetic data?

Synthetic data enhances privacy, allows for fast and large-scale dataset generation, improves test coverage, supports safe collaboration, and enables organizations to comply with data protection laws without compromising data utility.

6. Can synthetic data be used for testing and development?

Yes. Synthetic data is ideal for non-production environments like testing and development, especially when using real data would introduce privacy, security, or compliance risks. It provides realistic values and structure without exposing real individuals or customers.

7. How is synthetic data generated?

Synthetic data is generated using statistical modeling, simulation engines, or machine learning techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These tools learn patterns from real data and produce new data that follows similar distributions.

8. What are the challenges of using synthetic data?

Challenges include difficulty in accurately replicating complex real-world behavior, validating the statistical quality of the generated data, and requiring advanced tools or models to generate high-quality results for complex data types like text, images, or longitudinal records.

9. How do you validate synthetic data?

Validation involves comparing the synthetic dataset’s distributions, correlations, and outputs against the original real dataset. Statistical similarity testing, bias checks, and use-case-specific performance evaluations help determine if the synthetic data is accurate and usable.

10. What industries benefit most from synthetic data?

Industries that handle sensitive or regulated data—such as healthcare, banking, insurance, and government—benefit significantly. These sectors often face legal constraints when using real data for development or analytics, making synthetic data a safe and scalable alternative.

11. How does synthetic data help with data privacy regulations?

Synthetic data allows organizations to de-risk data usage by eliminating real PII or PHI, making it easier to meet privacy laws like GDPR, HIPAA, and CPRA. It enables testing, research, and data sharing without requiring complex consent or anonymization procedures.

12. Can synthetic data replace real data in testing?

Yes, in many cases synthetic data can fully replace real data for functional testing, performance validation, and even machine learning training. However, synthetic data should be validated to ensure it accurately represents the business logic and edge cases present in the real system.

13. How is IRI RowGen used to generate synthetic test data?

IRI RowGen creates structurally and referentially accurate synthetic test data based on real data models, metadata, and business rules. It generates high-volume, production-safe data for database, file, and report testing—all without exposing sensitive information.

14. What makes IRI RowGen different from other synthetic data tools?

IRI RowGen focuses on speed, scalability, and realism by generating sorted, constraint-aware test data using metadata or DDL files. Unlike general-purpose AI generators, it creates usable test data tailored for enterprise systems, QA pipelines, and data integration environments.

15. Can synthetic data help reduce dependency on production data?

Yes. Using synthetic data significantly reduces reliance on production environments for development or testing, mitigating risks of leaks, compliance issues, or system crashes caused by unmasked or improperly cloned production data.

16. How is synthetic data different from test data management?

Synthetic data generation is a method used within test data management (TDM). TDM involves sourcing, provisioning, and managing test data—synthetic generation is one of the most secure and scalable ways to provide test data without privacy concerns.

17. What are best practices for using synthetic data?

Best practices include ensuring diversity and coverage in synthetic datasets, validating them against real-world benchmarks, using advanced modeling techniques for realism, and integrating synthetic data generation into your TDM or DevOps pipelines for automated provisioning.

18. Can synthetic data support automation in testing?

Yes. Synthetic data generation can be automated within CI/CD pipelines to provision fresh, secure datasets for test cases. Tools like IRI RowGen support on-demand generation of high-volume test data that aligns with real business rules and formats.

19. How does synthetic data support AI safety and bias mitigation?

Synthetic data can be engineered to balance representation across demographic groups, mitigate edge case exclusion, and test model performance under rare conditions—supporting ethical AI development and reducing unintentional algorithmic bias.

20. What is the difference between fully synthetic and partially synthetic data?

Fully synthetic data contains no elements of real data and is generated entirely from scratch. Partially synthetic data blends synthetic values with real data elements to protect sensitive fields while preserving data continuity and contextual accuracy.