As artificial intelligence (AI) continues to evolve, data remains at the core of innovation. However, as regulations tighten and concerns around data privacy grow, many industries need help accessing sufficient real-world data. This is where artificially generated, but highly realistic synthetic data becomes crucial. The ability to generate simulated data that mirrors real-world datasets opens many possibilities for organizations in machine learning, data science, and beyond.

But for synthetic data to be truly effective, it must meet high-quality standards, mimic real-world complexity, and align with data privacy regulations.

This blog, which takes off right where we left our previous guide on the fundamentals of synthetic data , delves into how synthetic data can maintain data quality, some of its challenges, and the tools that can help future-proof AI models.

Challenges and limitations of synthetic data

While synthetic data offers numerous benefits, it also comes with challenges. Ensuring the quality, realism, and utility of artificial data is crucial to making it a viable solution for AI development.

Realism and fidelity issues

One of the primary challenges with synthetic data is achieving high realism and fidelity. Despite advances in deep learning techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), it can still be difficult to generate fake data that perfectly mirrors the complexity and nuances of real-world datasets.

For example, in fields like healthcare or finance, where the stakes are high, subtle patterns and correlations in the data are critical. If synthetic data fails to capture these details, AI models trained on the simulated data may not perform as expected when deployed in real-world applications. This can lead to poor outcomes and may limit the trust that organizations place in synthetic data.

To ensure that synthetic data accurately represents real-world data, several statistical validation techniques are commonly used:

• Kullback-Leibler divergence: This method measures the difference between synthetic and real data's probability distributions. A lower divergence score indicates that the artificial data is closely aligned with real data in terms of structure and variability. It's widely used to ensure that the generated fake data retains the characteristics of the original dataset.
• Jensen-Shannon divergence: A symmetric version of KL divergence, this method is particularly useful for validating synthetic data. It measures the similarity between the real and simulated data distributions. The result is a value between 0 and 1, where 0 means perfect similarity. It's less sensitive to outliers than KL divergence and offers more stable validation.
• Wasserstein distance : Also known as Earth Mover’s Distance (EMD), this metric calculates how much work is needed to transform one distribution into another. It’s especially effective in validating deep learning models, particularly for complex, high-dimensional datasets like images or time-series data. Wasserstein distance helps ensure that fake data is realistic enough for AI model training.

By applying these techniques, organizations can ensure that their synthetic data accurately reflects the distributions and patterns of real-world datasets.

Validation complexity

Validating synthetic data is more challenging than validating real-world data. For real data, validation typically involves checking for accuracy, completeness, and consistency. However, synthetic data requires an additional layer of validation—ensuring that it statistically aligns with real-world data and performs well in data modeling tasks.

Metrics like Jensen-Shannon divergence and Wasserstein distance are helpful, but they require expertise to interpret correctly. Moreover, it’s essential to continuously test synthetic data against real data to ensure that it maintains high data quality over time.

Computational resources required

The data generation process for synthetic data is computationally intensive. Techniques like GANs and VAEs require significant computational power, particularly when generating high-dimensional data such as images or videos. Smaller organizations may find it difficult to access the necessary resources, limiting their ability to leverage synthetic data effectively.

Access to high-performance hardware, such as GPUs and TPUs, is often required to generate large-scale synthetic data. Without these resources, organizations may struggle to generate artificial data at the scale needed for robust data analysis and data science projects.

Best practices for synthetic data generation

To fully leverage the advantages of synthetic data while minimizing potential challenges, it is essential to follow industry best practices. These strategies ensure that the data generation process yields high-quality artificial data that is reliable, secure, and compliant with data privacy regulations.

Data quality assurance

Ensuring the data quality of the synthetic data is critical, as poor-quality data can lead to inaccurate models and faulty decision-making. To maintain highdata quality, organizations should implement regular validation procedures:

• Statistical validation techniques: Use methods like Kullback-Leibler divergence, Jensen-Shannon divergence, and Wasserstein distance to compare fake data with real datasets. These techniques confirm that the synthetic data accurately reflects the distribution and relationships found in real-world data.
• Domain-specific testing: For complex industries like healthcare and finance, it's important to incorporate domain expertise in the validation process. By simulating real-world edge cases and domain-specific scenarios, you can ensure that the simulated data is relevant and useful.
• Continuous monitoring: As models evolve, so should your synthetic data. Implement ongoing checks to ensure the artificial data stays relevant and adapts to new trends or data sources in your industry.

Hybrid approaches (combining real and synthetic data)

A hybrid approach to data generation, mixing real and synthetic data, can enhance machine learning models and reduce bias. This strategy leverages the strengths of both data types for better model performance and broader applicability:

• Data augmentation: Use synthetic data to augment real datasets, particularly in areas with limited real-world data. For example, in data modeling tasks with imbalanced datasets, you can use simulated data to generate examples of underrepresented classes, improving the model’s ability to generalize.
• Pre-training and fine-tuning: Models can be pre-trained on large synthetic data sets and then fine-tuned with real-world data. This approach helps speed up the model-building process while ensuring that the final product is optimized for real-world use.
• Scenario simulation: Use synthetic data to simulate rare or complex scenarios that are difficult to replicate in real-world conditions. For instance, generating simulated data for autonomous vehicles to handle extreme weather or traffic conditions helps develop robust systems ready for deployment.

Continuous validation and refinement

The process of generating synthetic data should be dynamic, with ongoing validation and refinement to keep the data aligned with real-world trends. This iterative approach involves:

• Feedback loops: Monitor the performance of models trained on synthetic data and use the feedback to adjust your data generation processes. For example, if a model underperforms on certain tasks, you can refine the simulated data to better represent the edge cases or complexities that the model struggles with.
• Statistical and performance metrics: Validate synthetic data using both statistical metrics (such as Wasserstein distance and Jensen-Shannon divergence) and performance metrics from the models trained on that data. This ensures that the artificial data is not only statistically sound but also functionally effective in real-world applications.
• Iterative improvement: Continuously refine data generation techniques, whether by improving neural networks like Generative Adversarial Networks (GANs) or incorporating new data sources to enhance variability and realism.

Ethical considerations

Ethical use of synthetic data is paramount, particularly when dealing with sensitive domains like healthcare and finance. Despite the advantages of synthetic data, it’s important to mitigate any potential biases or ethical risks in the data generation process:

• Bias mitigation: While synthetic data can reduce bias in real-world datasets, it can also introduce new biases if not carefully managed. Ensure that the generative models used to create simulated data are designed to account for underrepresented populations and scenarios, promoting fairness in AI models.
• Transparency and explainability: Be transparent about how your synthetic data is generated, especially when used in regulated industries. Document the assumptions, processes, and limitations of the data generation techniques to ensure accountability and trustworthiness in AI systems.
• Data privacy protection: Even though synthetic data does not contain personal identifiers, it is important to ensure that the simulated data does not inadvertently recreate sensitive patterns that could compromise data privacy. This is especially important when generating synthetic data from small or vulnerable populations.

Tools and frameworks for synthetic data generation

The rise of synthetic data has been accompanied by the development of a range of tools and frameworks that streamline the data generation process, ensuring compliance with data privacy regulations and enhancing scalability.

Open-source tools

• SDV (Synthetic Data Vault): An open-source library from MIT's Data to AI Lab, SDV is widely used for generating synthetic tabular, time-series, and relational data. Its versatility and statistical validation tools make it a strong choice for both research and industry applications.
• CTGAN (Conditional Tabular GAN): A model designed specifically for generating tabular synthetic data, CTGAN excels at handling categorical variables and imbalanced datasets. It is widely applied in sectors like healthcare, finance, and data analytics, where high-quality tabular data is essential for training models.

Commercial platforms

• Mostly AI: This platform is geared towards generating privacy-compliant synthetic data for sectors like banking, insurance, and healthcare. Mostly AI ensures that generated artificial data retains the statistical properties of real-world data while aligning with privacy policy regulations like GDPR.
• Syntho: A commercial tool designed to generate synthetic data at scale, Syntho offers automated validation checks and integration with existing data pipelines, making it easy for businesses to adopt synthetic data without disrupting their workflows.
• Tonic.ai: Known for its ability to generate simulated data for software development and testing, Tonic.ai helps engineering teams create realistic test data that mirrors production environments. This enables quicker iterations and safer testing of AI models.

Future trends in synthetic data

As AI systems continue to evolve, synthetic data is set to play an even larger role in shaping the future of machine learning and data science. Several emerging trends are worth noting as data generation technologies advance.

Federated learning with synthetic data

Federated learning allows organizations to collaboratively train models without sharing their raw data, which is particularly useful in industries with stringent data privacy regulations. By combining federated learning with synthetic data, businesses can train models across multiple datasets without compromising data privacy.

• Data augmentation: Synthetic data can be used to supplement real-world datasets in federated learning models, balancing out data disparities between different participants.
• Privacy-preserving collaboration: Federated learning combined with synthetic data allows organizations to share insights without violating data privacy rules, making collaboration across borders and industries more secure.

Quantum computing for data generation

As quantum computing evolves, it promises to transform data generation processes by providing unprecedented computational power. Quantum algorithms could unlock new ways to generate synthetic data, making it even more realistic and diverse.

• Quantum-enhanced models: Quantum deep learning models could significantly improve the realism of synthetic data by generating more complex and high-dimensional datasets.
• Faster simulations: Quantum computing could enable faster simulations of systems, producing synthetic data that is more accurate and valuable for industries like climate modeling and drug discovery.

Edge AI and synthetic data

The rise of Edge AI—AI processing on edge devices like smartphones, IoT systems, and autonomous vehicles—requires specialized datasets. Synthetic data is ideal for Edge AI applications because it allows on-device data generation without transmitting sensitive data to the cloud, reducing both latency and data privacy concerns.

• On-device data generation: Edge devices can generate and process synthetic data locally, allowing AI models to learn and adapt in real-time without the need for constant cloud connectivity.
• Privacy and security: By keeping data generation on-device, Edge AI systems can maintain high levels of data security while complying with privacy policy requirements, especially in sectors like healthcare and finance.

Conclusion

Synthetic data has become a transformative force in the development of AI and machine learning models, addressing key challenges like data privacy, data scarcity, and cost-efficiency. Its ability to generate high-quality, privacy-compliant datasets opens new possibilities for industries ranging from healthcare to autonomous vehicles.

However, to fully unlock the potential of synthetic data, it’s essential to follow best practices, implement ongoing validation, and leverage cutting-edge tools. Organizations must also remain vigilant in addressing ethical concerns, such as bias mitigation and transparency, to ensure that the synthetic data they use is both fair and reliable.

At EnLume, we specialize in creating tailored synthetic data solutions that empower businesses to innovate while maintaining the highest standards of data security and compliance. Whether you are developing AI models, conducting data analysis, or looking for ways to scale your business, our expertise in data generation and data privacy ensures you can achieve your goals with confidence.

Contact EnLume today to learn how our synthetic data capabilities can help you build better, more secure AI systems for the future.