What is Synthetic Data?

• Synthetic data enables organizations to use data safely by replicating real-world patterns without exposing sensitive or personal information.
• A strong synthetic data approach accelerates AI development by reducing data access constraints and improving model training coverage.
• Synthetic data supports regulatory compliance by minimizing privacy, security, and data-sharing risks across the enterprise.
• Organizations use synthetic data to test, validate, and scale AI systems faster while maintaining data quality and governance standards.

Synthetic data refers to artificially generated data that statistically resembles real-world data without directly containing information from actual individuals, transactions, or events. It is created using algorithms that learn patterns, relationships, and distributions from real datasets and then generate new, artificial records that preserve those characteristics. For large organizations, synthetic data provides a practical solution to data scarcity, privacy, and access constraints.

From a strategic perspective, synthetic data matters because data availability is one of the biggest bottlenecks in analytics and AI initiatives. Many high-value use cases are delayed or blocked due to privacy regulations, data ownership issues, or limited historical data. Synthetic data allows organizations to unlock value from data while avoiding direct exposure of sensitive information.

Operationally, synthetic data improves speed and scalability. Teams can access data faster, share it more broadly across functions or partners, and experiment without lengthy approval processes. This accelerates model development, testing, and validation, especially in complex environments with strict compliance requirements.

Finally, synthetic data strengthens resilience and innovation. By enabling safe experimentation and robust testing, organizations can build and deploy more reliable analytics and AI systems while reducing legal, reputational, and operational risk.

Synthetic data can take several forms depending on how closely it mirrors real data and how it is generated. One common type is fully synthetic data, where entire datasets are artificially created without any direct linkage to individual real records. Fully synthetic data offers the strongest privacy protection and is often used for external data sharing, testing, and early-stage experimentation.

Another type is partially synthetic data. In this approach, sensitive attributes within real datasets are replaced or augmented with synthetic values, while non-sensitive information remains unchanged. This allows organizations to preserve high analytical utility while reducing privacy risk, particularly in regulated domains.

A third type is hybrid synthetic data, which combines real and synthetic records within the same dataset. Hybrid approaches are often used to balance realism and privacy, especially when certain rare patterns are difficult to generate synthetically. However, they require careful governance to avoid re-identification risks.

The choice of synthetic data type depends on use case, risk tolerance, and regulatory requirements. Large organizations often apply different approaches across development, testing, and production environments to balance utility, control, and privacy.

Synthetic data generation relies on a range of techniques that vary in complexity, realism, and controllability. One common approach is statistical modeling, where distributions, correlations, and constraints are learned from real data and used to generate new records. This method is relatively transparent and well-suited for structured enterprise data such as transactions, customer attributes, and operational metrics.

More advanced approaches use machine learning models to capture complex patterns that are difficult to express through simple statistical assumptions. Techniques such as generative adversarial networks and variational autoencoders can learn non-linear relationships and produce highly realistic synthetic datasets. These methods are valuable when data is high-dimensional or when realism is critical for downstream model performance.

Rule-based generation is another practical method. Domain experts define logical rules and constraints synthetic data must follow, such as business policies, physical limits, or process flows. While less flexible than generative models, rule-based approaches provide strong control, making them useful for deterministic testing, system validation, and scenario construction.

In practice, large organizations often combine these approaches. Hybrid generation pipelines use statistical methods for baseline realism, generative models for complex relationships, and rules to enforce business consistency, ensuring synthetic data is both useful and governed.

• Learning statistical distributions from real datasets and sampling new records accordingly.
• Training generative models to capture complex relationships and realistic variations within the data.
• Applying domain rules and constraints to enforce realism, consistency, and compliance requirements.

Synthetic data offers substantial benefits for large organizations, particularly in environments where data access, privacy, and scalability constrain analytics and AI initiatives. One of the most important benefits is enhanced privacy protection. Because synthetic data does not directly contain personal or sensitive information, it significantly reduces the risk of data breaches and regulatory violations. This makes it easier to use data across teams, geographies, and external partners without exposing confidential information.

Another major benefit is increased data availability and speed. Synthetic data can be generated on demand, eliminating long approval cycles associated with accessing real data. This accelerates experimentation, model development, and testing, enabling organizations to move faster from concept to deployment. For large enterprises, this speed advantage often translates into shorter time to value for analytics and AI investments.

Synthetic data also improves coverage and robustness. Real-world datasets often underrepresent rare events, edge cases, or extreme scenarios that are critical for model performance. Synthetic data can be deliberately generated to include these situations, improving model resilience and reducing the risk of unexpected failures in production. This is particularly valuable in areas such as fraud detection, risk management, and operational forecasting.

However, synthetic data has important limitations. The quality of synthetic data depends entirely on the quality of the source data and the generation method. If underlying data is biased, incomplete, or outdated, synthetic data will replicate those weaknesses. Poorly generated synthetic data can distort relationships, omit critical signals, or introduce artificial patterns that do not exist in reality.

There is also a risk of misuse or overreliance. Synthetic data should not be treated as a universal replacement for real data. Certain use cases, such as regulatory reporting or final decision-making, may still require real-world data. Without clear guidelines and validation, organizations may develop false confidence in models trained predominantly on synthetic data.

Using synthetic data responsibly at scale requires a deliberate and structured approach that integrates governance, quality management, and strategic intent. The starting point is clear use case definition. Organizations must explicitly define where synthetic data is appropriate, such as model training, stress testing, scenario simulation, or external data sharing, and where real data remains essential. This clarity prevents unrealistic expectations and misuse.

Governance plays a central role in responsible synthetic data use. Synthetic data should be treated as a managed enterprise data asset, with clear ownership, documentation, and approval processes. Organizations should define standards for generation methods, acceptable privacy thresholds, and validation requirements. This ensures synthetic data use is consistent, auditable, and aligned with regulatory expectations.

Quality assurance is another critical pillar. Responsible use of synthetic data requires systematic validation against real data using statistical similarity measures, business rules, and downstream model performance tests. Validation should not be a one-time activity. As real-world patterns change, synthetic data generation processes must be reviewed and updated to avoid drift and degradation.

Integration into data and AI operating models is equally important. Synthetic data should be embedded into existing data pipelines, tooling, and lifecycle management processes rather than treated as an ad hoc solution. This includes version control, access management, lineage tracking, and performance monitoring, all of which support transparency and accountability.

Finally, organizations should view synthetic data as a long-term strategic capability. As data regulations tighten and demand for AI accelerates, synthetic data enables scalable innovation without compromising trust. Organizations that invest in governance, skills, and tooling can use synthetic data to unlock value faster, share data safely, and build more resilient analytics and AI systems over time.