7 Best Practices for Synthetic Data Generation

In the rapidly evolving AI landscape, synthetic data has emerged as a powerful solution to address challenges such as data privacy, scarcity, bias and even to improve overall data quality of a given dataset. However, generating synthetic data isn't just about creating random numbers that mimic real-world data. It's a sophisticated process that involves training a generative model to produce data that mirrors the statistical properties and relationships found in actual datasets. Because of this complexity, following best practices is essential to ensure that the synthetic data you generate is both realistic and useful.

In this blog post, we will explore the best practices for structured synthetic data generation, focusing on how to effectively configure the process and ensure that the resulting data is fit for your intended purpose. From understanding the use case to validating the data and ensuring privacy, we'll cover the key steps necessary to create high-quality synthetic datasets that can seamlessly integrate into your data-driven projects.

Best practices

1. Understand the use case

Before diving into synthetic data generation, it's crucial to have a clear understanding of the specific use case. Whether you’re generating data for training machine learning models, testing algorithms, or validating data pipelines, the purpose will dictate the structure, scale, and fidelity of the synthetic data.

Key Considerations:

• What are the data characteristics required (e.g., size, format, and distribution)?
• Are there specific privacy concerns or regulations to address?
• What are the critical variables, relationships and distributions behavior that need to be preserved?

Best Practices:

• Understand if your use case is privacy or data improvement.
• Know the expected business outcome and objective

2. Understand the use case

Setting and configuring a concise and business aligned dataset schema is crucial for generating high-quality synthetic data. The schema should mirror the structure of the real-world data you aim to emulate, while ensuring the selected PII Types and Data Types are aligned with the use-case and applications. 

However, selecting the right process for generating the data within this schema is equally important. Unique identifiers, such as user IDs or transaction IDs, can hinder the quality of the synthetic data if they are included in the data generation process. These identifiers are often arbitrary and don’t carry meaningful information for the generative model to learn. Including them can lead to overfitting or unnecessary complexity in the synthetic data.

Best Practices:

• Data types: Make sure that you always check Data types prior to the synthetic data process. After all learning a "Category" is a different from learning the distribution for a *Numerical* variable.
• Include constraints such as primary keys, foreign keys, and data types to maintain data integrity. Also, make sure to configure the relation between tables (eg. x= a + b) as it will ensure that the model will treat the outcome for variable x as a deterministic process.
• Exclude Unique Identifiers: Do not include unique identifiers as features to be learned by the generative model. Instead, generate these identifiers separately or replace them with randomized values that do not affect the synthetic data's overall structure.

3. Avoid overfitting the original data

One of the risks when generating synthetic data is overfitting to the original dataset. This can occur if the generative process is too tightly bound to the specific examples in the training data, leading to synthetic data that is not sufficiently generalized.

What Not to Do:

• Avoid Excessive Fine-Tuning: Over-optimizing the generative model on the training data can lead to overfitting, making the synthetic data too similar to the original data and reducing its generalizability.
• Don’t Ignore Variability: Ensure that the synthetic data introduces enough variation to cover edge cases and rare events, rather than just replicating common patterns from the training data.

Fabric’s synthetic data generation process leverages the concept of Holdout in order to avoid overfitting, but the effectiveness of the holdout might vary depending on the dataset behavior and size.

4. Ensure Data Privacy

A primary reason for using synthetic data is to mitigate privacy risks. However, care must be taken to ensure that the synthetic data does not inadvertently reveal sensitive information from the original dataset, a phenomenon known as "data leakage."

What Not to Do:

• Don’t Reuse Identifiable Information: Avoid using direct identifiers (like names, addresses, etc.) in the synthetic data. Having a true identifier among the synthetic data might not only hinder the quality of the synthetic data but also its capacity to remain anonymous.

• Avoid Overfitting to Sensitive Data: Overfitting can also compromise privacy if synthetic data too closely resembles individual records from the original dataset. Ensure that the process introduces sufficient randomness or noise to obscure sensitive details.

5. Validate the synthetic data

Generating synthetic data is only half the battle; validating its utility and quality is equally important. The synthetic data should undergo rigorous testing to ensure that it meets the required criteria for the intended use case.

What Not to Do:

• Don’t Skip Statistical Validation: Failing to compare the statistical properties of the synthetic data against the real data can lead to datasets that are either too unrealistic or too similar to the original, defeating the purpose.
• Avoid Using Only One Metric: Relying on a single metric or validation method can provide a skewed view of the data’s quality. Ensure that you validate across multiple dimensions, such as distribution, correlation, and predictive performance.

YData Fabric synthetic data generation process offers an extensive and automated synthetic data quality report and profiling compare to help with the data quality validation.

Best practices:

• Use visualization: Leveraging diagrams to assess quality of data is easier for humans to spot errors or mistakes rather than relying on metrics only.

6. Iterate and refine

Synthetic data generation is an iterative process. Initial datasets may require refinement to improve their utility, accuracy, or realism. Continuous feedback and iteration are key to achieving high-quality synthetic data.

What Not to Do:

• Don’t Treat the First Version as Final: The first generated dataset is rarely perfect. Avoid the temptation to stop after the initial run, as refining the process can significantly improve data quality.
• Avoid Ignoring Feedback: Feedback from domain experts and end-users is invaluable. Disregarding this input can lead to synthetic data that fails to meet practical needs.

Best practices:

• Use MLOps and data pipelines for fast experimentation and iterations

7. Document and share

Finally, thorough documentation is essential for transparency, reproducibility, and collaboration. Document the data generation process, including the tools, models, parameters, and assumptions used. Sharing synthetic datasets and methodologies can also contribute to the broader community by enabling benchmarking and further research.

What Not to Do:

• Don’t Skip Documentation: Failing to document the synthetic data generation process can make it difficult to reproduce results or understand the reasoning behind certain decisions.
• Avoid Keeping the Process Opaque: Transparency is crucial, especially when synthetic data is used in critical applications. Ensure that all relevant details are clearly documented and accessible to stakeholders.

Conclusion

Synthetic data generation is a powerful tool that, when done correctly, can significantly enhance your data science and machine learning projects. By following these best practices—understanding the use case, defining the data schema, avoiding overfitting, ensuring privacy, validating results, iterating on the process, and documenting thoroughly—you can create synthetic datasets that are both realistic and useful.

Whether you're looking to overcome data scarcity, enhance privacy, or improve model robustness, synthetic data offers a flexible and effective solution. As the field continues to evolve, staying updated with the latest techniques and tools will ensure that your synthetic data generation efforts remain cutting-edge and impactful.

By adhering to these best practices and being mindful of what to avoid, you can unlock the full potential of synthetic data and drive innovation in your projects, all while safeguarding privacy and maintaining data integrity.

Cover Photo Google DeepMind on Unsplash