1 Executive Summary

The term synthetic data stands for artificially generated data that closely replicate the statistical properties, patterns, and characteristics of the real data. This replication mimics reality without including actual information about individuals or entities. As such, it becomes a secure and privacy preserving alternative to using raw, sensitive, or proprietary data. This data is used in training, testing, validation, and analytics. Artificial intelligence (AI) uses advanced algorithms to generate these datasets, preserving the statistical integrity of original data sources without exposing private information.

Thus, this Leadership Compass analyzes the solutions on the market that serve the following use cases for synthetic data:

• Privacy protection of production data, test data, and for datasets used to train machine learning and generative AI.
• Generating training data for AI/Machine Learning (ML) model training, testing, and validation.
• Dataset improvement, including data augmentation and diversity, bias mitigation of datasets and resulting models.
• Improve the security of data being stored and shared within the organization and with external parties.
• Other common business cases that benefit from heightened privacy and security such as generating user behavioral profiles, data sharing and monetization of datasets, demos, Proof of Concepts, and simulations (including attack, phishing, video and image simulations).

It is important to clarify that the use of synthetic data is not limited to the above-mentioned use cases. This is a dynamic market, and there will be more use cases, particularly business cases that arise as the use of synthetic data becomes more familiar and accepted.

Synthetic data solutions are versatile and can be applied to use cases across multiple industries including telecommunications, healthcare, financial institutions, government agencies, academic institutions, and more. Organizations equipped with synthetic data can leverage their other data-driven technologies by using AI and ML to accelerate business outcomes and elevate the privacy and security of their datasets. Considering that the demand for data protection continues to increase and that the ease of generating synthetic data to augment real datasets or allow analysis to occur without access to real-world data, the adoption of synthetic data will revolutionize data analysis in all sectors.

1.1 Highlights

• The synthetic data market is rapidly evolving with strong capabilities for enhancing data privacy and security.
• Advanced algorithms and ML models enable the creation of highly realistic synthetic datasets that maintain the statistical properties of the original data without compromising privacy.
• Synthetic data is increasingly being adopted to address the challenges of data scarcity and bias in ML and AI applications.
• Innovation in synthetic data technology is driven by the need to support diverse use cases, including testing, training ML models, and preserving privacy.
• Synthetic data generation techniques can be a means to bring datasets with sensitive information into compliance with stringent regulations, such as GDPR and CCPA.
• Most vendors offer comprehensive assessment tools for understanding customer needs and tailoring synthetic data solutions accordingly.
• Synthetic data solutions facilitate secure data sharing and collaboration across different organizations and geographical locations.
• Many vendors in this report have expertise in generating synthetic data for healthcare, telecom, finance, and retail industries.
• Vendors support primarily traditional on-premises data infrastructure, but some take a cloud-based or cloud-native approach.
• Despite the rapid growth, there is still a need for standardization and best practices to ensure the quality and reliability of synthetic data solutions.
• Overall leaders in the Synthetic Data market are CloudTDMS, AWS, Windocks, GenRocket, and YData.