The synthetic data generation market is expected to grow rapidly, reaching $13 billion by 2034

Integration synthetic data generation market The global industry is expected to reach USD 0.3 billion by 2024 due to renewable energy systems. As a result of this trend, the market is expected to see new opportunities, with a projected CAGR of 45.9% during 2024-2034 and a total value of around USD 13.0 billion by that year.

Throughout the data lifecycle, synthetic data production can be used with privacy-preserving technologies such as homomorphic encryption, differential privacy, and federated learning to guarantee data confidentiality and integrity. By improving data privacy and security in collaborative and distributed situations, privacy-preserving techniques are a valuable addition to synthetic data production.

Educational programs and training initiatives focused on synthetic data generation techniques and applications enable data scientists, researchers, and practitioners to effectively leverage synthetic data to develop and evaluate models. Training programs facilitate knowledge sharing and skill development in synthetic data generation methodologies, driving innovation and adoption across industries.

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There is a growing need for synthetic data generation solutions that offer compatibility and interoperability across operating systems, cloud platforms, and data management systems, as organizations operate across multiple platforms and environments. Cross-platform compatibility enables seamless integration and deployment of synthetic data across infrastructures and applications.

The demand for real-time data generation capabilities is growing across industries, driven by the need for immediate insights and decision-making. Real-time synthetic data generation solutions enable organizations to generate dynamic data sets on the fly, supporting time-sensitive applications such as fraud detection, predictive maintenance, and situational awareness.

Industry-specific synthetic data generation solutions, tailored to the unique requirements and challenges of specific industries and domains, are gaining popularity. Industry-specific solutions address industry-specific use cases and regulatory compliance requirements by offering specialized features and functionality optimized for specific industries, such as healthcare, finance, automotive, and retail.

Key findings from the market research

• The value of the global synthetic data generation market in 2023 was estimated at USD 200.0 million.
• During 2019–2023, market demand grew at a CAGR of 50.5%.
• The Japanese market is expected to grow at a CAGR of 47.0% until 2034.
• In terms of data type, the tabular data segment will exhibit a compound annual growth rate (CAGR) of 45.7% through 2034.
• The Korean market is expected to grow at a CAGR of 47.3% until 2034.
• In terms of modeling type, the direct modeling segment is expected to account for a compound annual growth rate (CAGR) of 45.5% through 2034.

Generative adversarial networks have emerged as a powerful technique for generating synthetic data with increased realism and diversity. Integration with GANs enables the generation of high-fidelity synthetic data that closely resembles real-world data distributions, facilitating more accurate model training and evaluation, notes the FMI analyst.

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Competitive environment

The key players in the synthetic data generation market include Mostly AI, CVEDIA Inc., Gretel Labs, Datagen, NVIDIA Corporation, Synthesis AI, Amazon.com, Inc., Microsoft Corporation, IBM Corporation, and Meta.

Company portfolio

• Datagen specializes in synthetic data generation software for data-driven applications. Their platform allows users to create synthetic datasets with configurable attributes, enabling efficient testing and training of machine learning models.
• NVIDIA Corporation offers synthetic data generation tools and libraries as part of its GPU-accelerated computing platform. These tools enable developers to efficiently generate synthetic data for training deep learning models and performing various research tasks.

Segmentation analysis of the synthetic data generation market

Data type:

• Tabulated data
• Test data
• Image and video data
• Others

By modeling type:

• Direct modeling
• Agent-Based Modeling

Offering:

• Completely synthetic data
• Partially synthetic data
• Hybrid synthetic data

By application:

• Data protection
• Data sharing
• Predictive analytics
• Natural Language Processing
• Computer vision algorithms
• Others

By end use:

• BFSI
• Healthcare and Life Sciences
• Transport and logistics
• IT and Telecommunications
• Retail and e-commerce
• Production
• Consumer electronics
• Others

By region:

• North America
• Latin America
• west Europe
• Eastern Europe
• South Asia and Pacific
• East Asia
• Middle East and Africa