Synthetic Data Market

Synthetic Data Market Overview

The global Synthetic Data Market size was valued at USD 0.28 billion in 2023 and is predicted to reach USD 2.63 billion by 2030 with a CAGR of 38.2% from 2024-2030.

The synthetic data market, also known as artificial data market refers to the creation of artificial data that emulates the characteristics of real-world data without involving actual personal or event-based information. This data is generated using sophisticated algorithms and simulation techniques, offering significant advantages such as enhanced privacy protection, cost efficiency, and rapid access to large datasets.

Artificial data also enables the generation of balanced data sets, reducing biases and facilitating more accurate testing and training of machine learning models. As organizations increasingly seek solutions to overcome privacy and data acquisition challenges, the market is poised for continued growth, becoming a crucial component in the advancement of artificial intelligence and data-driven innovations.

Market Dynamics and Trends

The global shift towards digitalization across the globe is driving the synthetic data market growth as organizations embrace digital transformation and seek diverse data solutions to advance artificial intelligence (AI), machine language (ML) and other emerging technologies. As digitalization increases the volume and complexity of data, artificial data offers a crucial means to manage, simulate, and utilize this information effectively.

According to the latest report published by International Data Corporation (IDC) the spending for digital transformation is expected to reach USD 3.9 trillion by 2027 globally, growing at a five-year CAGR of 16.1%. The surge in investment towards digitalization, drives the demand for advanced data solution including artificial data that in turn boosts the growth of the synthetic data market demand.

Moreover, the rapidly expanding healthcare sector, coupled with the rising demand for data security and privacy, is significantly driving the growth of the market. As healthcare organizations increasingly adopt digital technologies and collect vast amounts of sensitive data, artificial data provides a valuable solution by simulating real-world data while protecting personal information.

As per the report published by the Ministry of Health and Welfare, the total budget for healthcare in India was around USD 10.69 billion in 2023. Furthermore, as per the Government of Australia, the total budget on healthcare sector in 2022 was around USD 71.9 billion. The significant investments in healthcare, combined with the demand for data security and privacy fuels the growth of the market.

Furthermore, the rising adoption of AI and machine learning (ML) technologies in the finance sector is boosting the growth of the synthetic data industry. As financial institutions seek extensive, high-quality data for applications such as fraud detection and risk assessment, artificial data offers a solution by providing simulated datasets that protect privacy and support effective model training. 

For instance, in June 2024, NVIDIA Corporation launched Nemotron-4, a suite of large language models (LLMs) designed to generate high-quality synthetic data for training robust AI systems across finance manufacturing and various industries. This development highlights the increasing significance of artificial data in supporting AI-driven innovation, further contributing to the synthetic data market expansion.

However, limited real-world variability and difficulty in validating the accuracy of data generated by the synthetic data are the major factors restraining the growth of the market. On the contrary, the introduction of latest technologies including synthetic open-source text-to-SQL is expected to create ample opportunities in the growth of the market. This dataset is designed to enhance AI capabilities by allowing businesses to generate and query databases using natural language.

Market Segmentations and Scope of the Study

The synthetic data market report is segmented on the basis of component, deployment mode, data type, application, end-user, and region. On the basis of component, the market is divided into solution and services. Based on deployment mode, the market is divided into on-premise and cloud.

On the basis of data type, the market is classified into tabular data, text data, image & video data, and others. On the basis of applications, the market is divided into AI training & development, test data management, data sharing & retention, data analytics, and others. Based on the end-user, the market is divided into BFSI, healthcare & life sciences, transportation & logistics, government & defense, IT & telecommunication, manufacturing, media & entertainment, and others. Regional breakdown and analysis of each of the aforesaid segments include regions comprising of North America, Europe, Asia-Pacific, and RoW.

Geographical Analysis

North America dominates the synthetic data market share and is expected to continue its dominance during the forecast period. This is attributed to factors such as growing healthcare and life science sector in this region, that is driving the demand for diverse privacy-preserving data for advancements in drug discovery, clinical trials, and personalized medicine. 

According to a report from the Centers for Medicare & Medicaid Services (CMS), the medical spendings in the U.S is growing in a significant rate. The national health expenditure rose to around USD 4.84 trillion in 2023 from USD 4.50 trillion in 2022, emphasizing the demand for innovative data solutions, driving the growth of synthetic data market trends.

Moreover, the media & entertainment sector in this region is driving the growth of the market as it leverages synthetic data to enhance content creation, audience analytics, virtual production, and immersive experiences such as VR and AR applications. 
According to the International Trade Administration, the U.S. Media and Entertainment (M&E) industry is the largest in the world at USD 660 billion in 2020.

On the other hand, Asia-Pacific is expected to show a steady growth in the synthetic data sector. This is attributed to the rising investment by the government of various countries towards digital transformation in this region. The Ministry of Commerce, set forward a comprehensive action plan to drive the digital transformation of its commercial sectors by 2026 in China.  This action plan highlights innovation, international cooperation, and initiatives to boost consumer spending in digital, green, and health-related sectors. The surge in digitalization while maintaining the sustainability standards drives the demand for artificial data, thereby fuelling the growth of the market.

Moreover, the growing fintech industry is driving the expansion of the market as financial technology companies require extensive, high-quality data for applications such as fraud detection and credit scoring. Synthetic data provide secure, simulated datasets that support accurate model development and compliance.

According to Invest India, the fintech industry was valued at USD 584 billion in 2022 and is estimated to reach USD 1.5 trillion by 2025. The surge in fintech industry drives the demand for artificial data for various application thereby driving the market growth.

Competitive Landscape

Various market players operating in the synthetic data industry include NVIDIA Corporation, Microsoft Corporation, International Business Machines Corporation, SAS Institute Inc., MOSTLY AI GmbH, K2View Ltd., Tonic Inc., Syntho BV, GenRocket Inc., MDClone Ltd., Synthesis AI, Inc., Parallel Domain, Inc., Rendered.ai Corporation, Anyverse S.L., Mindtech Global Ltd., and others. These market players are adopting various strategies such as acquisition, partnership, and collaboration to remain dominant in the market. 

For instance, in September 2025, NVIDEA Corporation launched new synthetic data generation tools within its Omniverse platform to accelerate AI model training and simulation accuracy.

For instance, in August 2025, Microsoft Corporation, Integrated synthetic data generation features into Azure AI Studio to help enterprises enhance model privacy and performance.

Furthermore, in September 2025, Tonic Inc.Released “Tonic AI Enterprise Edition” with synthetic data scaling for multi-cloud environments.

Key Benefits

• The market report provides the quantitative analysis of the current market and estimations from 2024 to 2030. This analysis assists in identifying the prevailing market opportunities to capitalize on.

• The study comprises of a detailed analysis of the current and future synthetic data market trends for depicting the prevalent investment pockets in the market.

• The information related to key drivers, restraints, and opportunities and their impact on the market is provided in the report.

• The competitive analysis of the market players along with their market share in the market is mentioned.

• The SWOT analysis and Porter’s Five Forces model are elaborated in the study.

• The value chain analysis in the market study provides a clear picture of the stakeholders’ roles.

Synthetic Data Market Key Segments

By Offering                    

• Solutions/Platforms                

• Services                

  • Professional Services            

  • Managed Services            

By Data Type                    

• Tabular Data                

• Text Data                

• Image and Video Data                

• Time Series and Sensor Data                

• Audio and Speech Data                

• Other Data Types                

By Generation Technique                    

• Simulation-Based Modeling                

  • Agent-Based Modeling            

  • Direct Modeling            

• Machine Learning-Based Generation                

  • Generative Adversarial Networks (GANs)            

  • Variational Autoencoders (VAEs)            

  • Diffusion Models            

  • Large Language Models (LLMs)            

• Statistical and Rule-Based Methods                

• Hybrid Approaches                

By Deployment Mode                    

• Cloud-Based                

• On-Premises                

By Application                    

• AI and ML Model Training                

• Data Privacy and Security                

• Test Data Management                

• Data Augmentation                

• Simulation and Digital Twins                

• Data Analytics and Visualization                

• Other Applications                

By Organization Size                    

• Large Enterprises                

• Small and Medium Enterprises (SMEs)                

By End-Use Industry                    

• Banking, Financial Services, and Insurance (BFSI)                

• Healthcare and Life Sciences                

• Retail and E-commerce                

• Transportation and Logistics                

• IT and Telecommunication                

• Automotive and Autonomous Vehicles                

• Manufacturing                

• Government and Defense                

• Media and Entertainment                

• Other Industries

By Region

• North America

  • The U.S.

  • Canada

  • Mexico

• Europe

  • The UK

  • Germany

  • France        

  • Italy        

  • Spain        

  • Denmark        

  • Netherlands        

  • Finland        

  • Sweden        

  • Norway        

  • Russia        

  • Rest of Europe    

• Asia Pacific

  • China

  • Japan

  • India

  • South Korea

  • Australia

  • Indonesia

  • Singapore

  • Taiwan

  • Thailand

  • Rest of Asia Pacific

• RoW

  • Latin America

  • Middle East

  • Africa

Key Players

• NVIDIA Corporation

• Microsoft Corporation

• International Business Machines Corporation

• SAS Institute Inc.

• MOSTLY AI GmbH

• K2View Ltd.

• Tonic Inc.

• Syntho BV

• GenRocket Inc.

• MDClone Ltd.

• Synthesis AI, Inc.

• Parallel Domain, Inc.

• Rendered.ai Corporation

• Anyverse S.L.

• Mindtech Global Ltd.

REPORT SCOPE AND SEGMENTATION