Synthetic Datamarket
Synthetic DataMarket Forecasts to 2032 – Global Analysis By Type (Fully Synthetic Data, Partially Synthetic Data, Hybrid Synthetic Data, Anonymized Synthetic Data and Other Types), Data Modality, Deployment, Technology, Application and By Geography
According to Stratistics MRC, the Global Synthetic Data Market is accounted for $419.8 million in 2025 and is expected to reach $3466.4 million by 2032 growing at a CAGR of 35.2% during the forecast period. Synthetic Data is artificially generated information that replicates the statistical properties and structures of real-world data without exposing sensitive details. Created using algorithms, simulations, or generative models, synthetic data mimics patterns, variability, and complexity found in actual datasets. It is widely used in training AI systems, testing software, and safeguarding privacy in data-sharing processes. Unlike anonymized data, synthetic datasets are built from scratch, ensuring both utility for analysis and protection against risks associated with personal data.
According to Gartner, synthetic data adoption is accelerating, with 60% of AI-driven enterprises projected to use it for model training by 2027.
Market Dynamics:
Driver:
Rising demand for AI training
Rising demand for AI training is significantly shaping the synthetic data market, as enterprises and research institutions increasingly require vast, diverse datasets to optimize machine learning models. Synthetic data provides scalability without privacy compromises, making it highly valuable for deep learning applications. Fueled by growing automation, digital transformation, and reliance on advanced AI models, organizations are leveraging synthetic datasets to simulate complex real-world scenarios, enhance model accuracy, and streamline innovation in artificial intelligence development.
Restraint:
Lack of standardization across industries
Lack of standardization across industries hampers the adoption of synthetic data, as organizations struggle with interoperability, validation, and compliance frameworks. Without unified benchmarks, concerns about reliability and comparability of artificially generated datasets persist. Spurred by fragmented adoption patterns, many enterprises hesitate to fully integrate synthetic data into critical applications. Consequently, inconsistent quality assurance and absence of global protocols act as significant barriers, restricting market expansion and slowing mainstream acceptance of synthetic datasets across sectors like finance, healthcare, and manufacturing.
Opportunity:
Expansion into healthcare AI applications
Expansion into healthcare AI applications presents a compelling growth opportunity for the synthetic data market, as hospitals and research labs require secure, anonymized datasets for model training. Influenced by strict patient data privacy regulations, synthetic datasets provide a solution for developing diagnostic algorithms, personalized medicine, and clinical simulations. Spurred by rising demand for precision health and regulatory compliance, synthetic data providers are increasingly collaborating with healthcare organizations to accelerate AI adoption, reduce risks, and enhance innovation in medical technologies.
Threat:
Competition from anonymized real datasets
Competition from anonymized real datasets poses a major threat to synthetic data adoption, as many organizations still prefer traditional anonymization methods for cost efficiency and familiarity. Propelled by long-standing regulatory acceptance, anonymized datasets are often viewed as sufficient for non-sensitive use cases, challenging synthetic data providers. However, anonymized data carries re-identification risks. Despite this, its entrenched use and lower integration hurdles create a competitive landscape where synthetic data solutions must continually demonstrate superior security, scalability, and reliability advantages.
Covid-19 Impact:
The COVID-19 pandemic accelerated digital adoption, propelling demand for secure and scalable synthetic datasets to simulate disruptions and support AI-driven decision-making. Remote work and online healthcare consultations required secure data handling, strengthening synthetic data adoption. Fueled by the surge in AI-based predictive models during the crisis, organizations leveraged synthetic datasets for healthcare research, supply chain resilience, and fraud detection. Consequently, the pandemic acted as a catalyst, reshaping the market landscape by highlighting the necessity of privacy-preserving, large-scale synthetic data solutions.
The fully synthetic data segment is expected to be the largest during the forecast period
The fully synthetic data segment is expected to account for the largest market share during the forecast period, propelled by its ability to generate entirely artificial datasets that eliminate privacy concerns. Unlike partially synthetic approaches, fully synthetic data ensures higher protection and adaptability across industries such as healthcare, finance, and retail. Its capacity to mirror statistical properties of real data while maintaining compliance standards makes it highly desirable, particularly in regulatory-driven sectors demanding robust privacy safeguards.
The image & video data segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the image & video data segment is predicted to witness the highest growth rate, influenced by the rapid expansion of computer vision, autonomous vehicles, and augmented reality applications. Synthetic visual datasets enable training of AI models without requiring millions of real-world images or footage. Fueled by growing demand for surveillance, healthcare imaging, and retail analytics, this segment is experiencing unprecedented adoption. Its versatility in replicating real-world complexity drives robust momentum in multiple industries.
Region with largest share:
During the forecast period, the Asia Pacific region is expected to hold the largest market share, fueled by its rapidly expanding digital ecosystem, increasing AI investments, and large-scale enterprise adoption. Countries like China, India, and Japan are at the forefront of implementing AI-based innovations across manufacturing, finance, and smart cities. With government support for artificial intelligence research and data localization policies, Asia Pacific demonstrates strong market leadership, creating a favorable environment for synthetic data expansion.
Region with highest CAGR:
Over the forecast period, the North America region is anticipated to exhibit the highest highest CAGR, driven by its advanced AI research ecosystem, strong presence of synthetic data startups, and increasing regulatory focus on data privacy. Fueled by collaborations between technology giants, academic institutions, and healthcare innovators, North America is witnessing strong uptake across diverse sectors. Its early adoption of cutting-edge AI models, combined with robust venture funding, positions the region as the fastest-growing hub for synthetic data innovation.
Key players in the market
Some of the key players in Synthetic Data Market include Mostly AI, Synthesis AI, Gretel.ai, Hazy, Cognitensor, MDClone, AI.Reverie, Datagen Technologies, Zebracat AI, Statice, Tonic.ai, Cauliflower, Sky Engine AI, Informatica, Microsoft and IBM Research.
Key Developments:
In August 2025, Mostly AI launched advanced domain-specific synthetic data generation platforms designed to produce highly realistic tabular and time-series datasets for healthcare and finance sectors.
In July 2025, Synthesis AI expanded its 3D synthetic image and video dataset portfolio with improved generative AI models supporting autonomous vehicle training and retail applications.
In June 2025, Gretel.ai unveiled privacy-enhanced synthetic data tools integrating differential privacy algorithms, helping enterprises meet GDPR and HIPAA compliance in data sharing.
Types Covered:
• Fully Synthetic Data
• Partially Synthetic Data
• Hybrid Synthetic Data
• Anonymized Synthetic Data
• Other Types
Data Modalities Covered:
• Tabular Data
• Text Data (NLP & Chatbots)
• Image & Video Data
• Audio Data
• Time-Series Data
• Multi-Modal Data
Deployments Covered:
• Cloud-Based Solutions
• On-Premises Solutions
• Hybrid Deployment
Technologies Covered:
• Generative Adversarial Networks (GANs)
• Agent-Based Models
• Transformer-Based Models
• Other Technologies
Applications Covered:
• Model Training & Testing
• Data Privacy & Security Enhancement
• Fraud Detection & Risk Management
• Healthcare & Genomics Research
• Autonomous Systems
• Other Applications
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 Application Analysis
3.8 Emerging Markets
3.9 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global Synthetic Data Market, By Type
5.1 Introduction
5.2 Fully Synthetic Data
5.3 Partially Synthetic Data
5.4 Hybrid Synthetic Data
5.5 Anonymized Synthetic Data
5.6 Other Types
6 Global Synthetic Data Market, By Data Modality
6.1 Introduction
6.2 Tabular Data
6.3 Text Data (NLP & Chatbots)
6.4 Image & Video Data
6.5 Audio Data
6.6 Time-Series Data
6.7 Multi-Modal Data
7 Global Synthetic Data Market, By Deployment
7.1 Introduction
7.2 Cloud-Based Solutions
7.3 On-Premises Solutions
7.4 Hybrid Deployment
8 Global Synthetic Data Market, By Technology
8.1 Introduction
8.2 Generative Adversarial Networks (GANs)
8.3 Agent-Based Models
8.4 Transformer-Based Models
8.5 Other Technologies
9 Global Synthetic Data Market, By Application
9.1 Introduction
9.2 Model Training & Testing
9.3 Data Privacy & Security Enhancement
9.4 Fraud Detection & Risk Management
9.5 Healthcare & Genomics Research
9.6 Autonomous Systems
9.7 Other Applications
10 Global Synthetic Data Market, By Geography
10.1 Introduction
10.2 North America
10.2.1 US
10.2.2 Canada
10.2.3 Mexico
10.3 Europe
10.3.1 Germany
10.3.2 UK
10.3.3 Italy
10.3.4 France
10.3.5 Spain
10.3.6 Rest of Europe
10.4 Asia Pacific
10.4.1 Japan
10.4.2 China
10.4.3 India
10.4.4 Australia
10.4.5 New Zealand
10.4.6 South Korea
10.4.7 Rest of Asia Pacific
10.5 South America
10.5.1 Argentina
10.5.2 Brazil
10.5.3 Chile
10.5.4 Rest of South America
10.6 Middle East & Africa
10.6.1 Saudi Arabia
10.6.2 UAE
10.6.3 Qatar
10.6.4 South Africa
10.6.5 Rest of Middle East & Africa
11 Key Developments
11.1 Agreements, Partnerships, Collaborations and Joint Ventures
11.2 Acquisitions & Mergers
11.3 New Product Launch
11.4 Expansions
11.5 Other Key Strategies
12 Company Profiling
12.1 Mostly AI
12.2 Synthesis AI
12.3 Gretel.ai
12.4 Hazy
12.5 Cognitensor
12.6 MDClone
12.7 AI.Reverie
12.8 Datagen Technologies
12.9 Zebracat AI
12.10 Statice
12.11 Tonic.ai
12.12 Cauliflower
12.13 Sky Engine AI
12.14 Informatica
12.15 Microsoft
12.16 IBM Research
List of Tables
1 Global Synthetic Data Market Outlook, By Region (2024-2032) ($MN)
2 Global Synthetic Data Market Outlook, By Type (2024-2032) ($MN)
3 Global Synthetic Data Market Outlook, By Fully Synthetic Data (2024-2032) ($MN)
4 Global Synthetic Data Market Outlook, By Partially Synthetic Data (2024-2032) ($MN)
5 Global Synthetic Data Market Outlook, By Hybrid Synthetic Data (2024-2032) ($MN)
6 Global Synthetic Data Market Outlook, By Anonymized Synthetic Data (2024-2032) ($MN)
7 Global Synthetic Data Market Outlook, By Other Types (2024-2032) ($MN)
8 Global Synthetic Data Market Outlook, By Data Modality (2024-2032) ($MN)
9 Global Synthetic Data Market Outlook, By Tabular Data (2024-2032) ($MN)
10 Global Synthetic Data Market Outlook, By Text Data (NLP & Chatbots) (2024-2032) ($MN)
11 Global Synthetic Data Market Outlook, By Image & Video Data (2024-2032) ($MN)
12 Global Synthetic Data Market Outlook, By Audio Data (2024-2032) ($MN)
13 Global Synthetic Data Market Outlook, By Time-Series Data (2024-2032) ($MN)
14 Global Synthetic Data Market Outlook, By Multi-Modal Data (2024-2032) ($MN)
15 Global Synthetic Data Market Outlook, By Deployment (2024-2032) ($MN)
16 Global Synthetic Data Market Outlook, By Cloud-Based Solutions (2024-2032) ($MN)
17 Global Synthetic Data Market Outlook, By On-Premises Solutions (2024-2032) ($MN)
18 Global Synthetic Data Market Outlook, By Hybrid Deployment (2024-2032) ($MN)
19 Global Synthetic Data Market Outlook, By Technology (2024-2032) ($MN)
20 Global Synthetic Data Market Outlook, By Generative Adversarial Networks (GANs) (2024-2032) ($MN)
21 Global Synthetic Data Market Outlook, By Agent-Based Models (2024-2032) ($MN)
22 Global Synthetic Data Market Outlook, By Transformer-Based Models (2024-2032) ($MN)
23 Global Synthetic Data Market Outlook, By Other Technologies (2024-2032) ($MN)
24 Global Synthetic Data Market Outlook, By Application (2024-2032) ($MN)
25 Global Synthetic Data Market Outlook, By Model Training & Testing (2024-2032) ($MN)
26 Global Synthetic Data Market Outlook, By Data Privacy & Security Enhancement (2024-2032) ($MN)
27 Global Synthetic Data Market Outlook, By Fraud Detection & Risk Management (2024-2032) ($MN)
28 Global Synthetic Data Market Outlook, By Healthcare & Genomics Research (2024-2032) ($MN)
29 Global Synthetic Data Market Outlook, By Autonomous Systems (2024-2032) ($MN)
30 Global Synthetic Data Market Outlook, By Other Applications (2024-2032) ($MN)
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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