What Are Synthetic Data and Their Uses?

In today's context, synthetic data have emerged as a crucial strategy in certain processes, with numerous instances where an organization might need them to protect contained information while accessing high-quality datasets.

Moreover, the Spanish Data Protection Agency recognizes the utility of synthetic data as a security strategy.

Gartner predicts that by 2024, at least 60% of the data used in analytics and Artificial Intelligence development projects will consist of synthetic datasets.

But, what exactly are synthetic data, and what roles do they play today? Here’s everything you need to know.

What Are Synthetic Data?

Synthetic data are datasets generated not from real sources but artificially using algorithms and data generation techniques.

First introduced in the 1990s by Harvard statistics professor Donald B Rubin, their significance has soared with the rise of Artificial Intelligence and machine learning, along with organizations' increasingly complex data analysis and usage needs.

Generated on demand, synthetic data can be tailored to meet the specific needs of a project.

Their quality largely depends on the algorithms generating them and the assumptions used in their creation. Hence, validating these data is crucial to ensure they are representative and, therefore, useful.

Uses of Synthetic Data

Synthetic data address several problems and situations organizations face:

The Privacy vs. Utility Dilemma

Organizations need to ensure data privacy while extracting value through analysis or internal productive processes. Synthetic data provide a way to generate useful and secure datasets.

Safety in Sharing Data with Third Parties

Synthetic data solve the issue of sharing inadequately anonymized datasets that risk privacy and might face legal issues under General Data Protection Regulation (GDPR).

Enough and Quality Data

Organizations turn to synthetic data when real data are insufficient for project needs. Moreover, synthetic data can meet conditions not found in original data, being generated on demand without relying on reality.

Thus, synthetic data prove useful in multiple processes, including research and development, business decision-making, software development, machine learning model training, software testing, security testing, or education.

Why Not Manually Create Them?

Businesses often waste time and money due to the absence of a tool for creating high-quality synthetic data when needed, resulting in hours spent manually creating or waiting for them. This poor data management can significantly impact productivity, staffing, test quality, and regulatory compliance.

Benefits of Using Synthetic Data

• Ensures privacy, surpassing anonymization processes
• Meets GDPR legislation and ARCO rights
• Generates quality datasets, even for specific contexts like test environments
• Enables the extraction of additional data value, thanks to the generation of large volumes of secure, useful data that can be easily shared and exchanged with other organizations.

Synthetic Data vs. Anonymized Data

They represent two different approaches to data privacy protection. Each has its benefits and limitations, recommended for use in varying situations.

Synthetic data may retain the utility of original data more effectively than anonymization, which can degrade data quality and utility, complicating their use in some contexts. Unlike some current anonymization techniques defined by GDPR as pseudonymization, synthetic data, being artificially generated, contain no direct information about individuals, significantly reducing the risk of reidentification.

Furthermore, synthetic data facilitate regulatory compliance in regulated industries by eliminating the risk of non-compliance due to poor anonymization.

Additionally, synthetic data offer greater flexibility, capable of representing a wider variety of situations and scenarios compared to anonymization. This can be beneficial for training algorithms or simulations requiring non-existent real-world data.

Finally, synthetic data allow more control over detail level and noise in datasets, tailored to specific application or research needs.

The choice between anonymization approaches and synthetic data depends on the data nature, privacy requirements, and applicable regulations in a particular context. In some cases, combining both strategies may be the most appropriate solution, decided by data set design experts.

How to Create Synthetic Data?

Simplified, synthetic data generation occurs via Machine Learning models trained on original data. These models can identify existing patterns and reproduce them, maintaining the statistical properties of the original data.

Tools like icaria Technology's TDM software can generate synthetic datasets focused on blocking and suppressing personal data in production environments. This tool handles Test Data Management processes comprehensively, enabling effective data management in development environments. Among its functions, it can generate secure, complete, and consistent synthetic datasets.

Interested in learning more about them and if they are the solution your project needs? At icaria Technology, we can assist you. Contact us and discover how.