A Comprehensive Guide to Synthetic Data Generation

Synthetic data is emerging as a crucial tool for enterprises needing to balance data privacy and utility. In this 2600+ word guide, we’ll provide an in-depth look at everything you need to know about synthetic data generation – from basic concepts to practical implementation tips.

What is Synthetic Data?

Synthetic data is artificially generated data that retains statistical properties of real data without containing any actual personal information. It serves as a privacy-preserving stand-in for real data.

Synthetic data can accurately model complex real-world data while being compliant for use cases with privacy restrictions. This balance of privacy protection and retained utility makes synthetic data valuable in domains like healthcare research, financial services, transportation modeling, and more.

Why is Synthetic Data Important?

Here are some of the key benefits driving adoption of synthetic data:

• Privacy Compliance: Synthetic data can be freely used under regulations like GDPR and CCPA that restrict use of real personal data
• No Data Breach Risks: Unlike real data, synthesized data carries no risks even if exposed or stolen
• Test/Dev Environments: Synthetic data can cost-effectively mimic production data at any scale needed for software testing/development
• Train ML Models: AI models like predictive analytics and credit risk models can be trained on synthetic data proxies
• Share Data Freely: Synthetic datasets can be shared across teams or publicly released to accelerate innovation

Essentially, synthetic data unlocks previously trapped value from private real-world data.

When Should You Use Synthetic Data?

You should consider using synthetic data in these four common scenarios:

In particular, highly regulated industries like healthcare, banking, insurance, and government agencies can benefit enormously from transitioning to synthetic data.

Synthetic data also empowers safe “debugging” of AI models detecting potentially dangerous or illegal content/activity by providing harmless proxy data instead.

How To Generate Synthetic Data

There are a variety of techniques used to algorithmically generate synthetic data proxies:

• Deep learning models like generative adversarial networks (GANs) and variational autoencoders (VAEs)
• Fitting parametric distributions then sampling data values
• Iterative proportional fitting to match joint distributions
• Combining anonymized real data with synthesized data (the hybrid approach)

Which technique you select depends on your data types, use case requirements, and machine learning expertise.

Let’s look at two leading approaches: GANs and fitting distributions.

Generative Adversarial Networks

GANs are an advanced deep learning technique for generating synthetic data. They work by training two competing neural networks against each other:

• Generator: Creates new synthetic data samples from noise
• Discriminator: Attempts to differentiate the synthetic samples from real samples

This adversarial competition causes the outputs to become increasingly realistic over many training iterations.

GANs have achieved impressive results but can be complex to develop and tune.

Fitted Distributions

A simpler approach is to fit parametric probability distributions to real data and then sample synthetic values from those fitted models.

The steps are:

1. Select distribution types based on data properties
2. Fit distribution parameters to real data
3. Sample synthetic values from distributions

Python libraries like SciPy and scikit-learn make this easy to implement. The hard part is choosing suitable distributions and assessing how well they capture all temporal/spatial relationships within the real data.

Best Practices For Synthetic Data

To maximize the utility and return on investment from synthetic data, keep these best practices in mind:

• Start with comprehensive data cleaning and ETL processes before synthesizing data
• Take time to rigorously evaluate synthetic vs real data to validate suitability
• Blend synthesized records with some anonymized real entities to improve quality
• Re-balance your models’ training datasets using synthetic oversampling for rare classes of data
• Leverage synthetic data early to shift left – enabling robust software testing and development

Also continuously monitor your synthetic data over time, re-training models as needed to catch drift or degradation issues.

Open Source Synthetic Data Tools

If going the open source route, these Python libraries enable synthetic data generation:

For structured data, the dbgen utility enables mocking real databases with millions of parametrized synthetic rows.

Featuretools is also worthwhile for automated feature engineering.

Synthetic Data As A Service

As an alternative to building in-house synthetic data engineering capabilities, turnkey cloud platforms provide synthetic datasets tailored to your needs:

These vendors offer enterprise-grade security, support for diverse data types, and accelerated ROI versus staffing data science teams.

The Future of Synthetic Data

We are still early in unlocking the immense possibilities of privacy-enhancing synthetic data.

Ongoing research and emerging techniques promise even higher-fidelity and more customizable synthetic data generation in the years ahead.

Already, synthesized training data is proving itself in mission-critical settings like healthcare AI and autonomous vehicles.

As leading enterprises demonstrate quantifiable value leveraging synthetic data, adoption will accelerate rapidly. In time, synthetic data may become the default choice balancing data privacy versus utility across most industries.