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市場調查報告書

商品編碼

1857028

全球合成資料生成市場：未來預測（至2032年）－依產品/服務、元件、資料類型、建模類型、部署方法、應用、最終使用者和地區進行分析

Synthetic Data Generation Market Forecasts to 2032 - Global Analysis By Offering, Component, Data Type, Modeling Type, Deployment Mode, Application, End User, and By Geography

出版日期: 2025年10月01日 | 出版商:

Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的數據，預計 2025 年全球合成數據生成市場規模將達到 6.2 億美元，到 2032 年將達到 79.3 億美元，預測期內複合年成長率將達到 43.9%。

合成資料生成技術能夠產生與真實資料統計特性相符的人工資料集，同時保護使用者隱私，從而在無需敏感生產記錄的情況下實現人工智慧的訓練、測試和分析。這有助於緩解標註數據稀缺的問題，減少數據偏差，並加速受監管行業的模型迭代。人工智慧/機器學習的廣泛應用、隱私法規的規範以及對多樣化、大規模標註資料集的需求，共同推動了合成資料生成技術的發展。

在隱私法規的背景下，對人工智慧/機器學習訓練資料的需求不斷成長。

隨著人工智慧 (AI) 和機器學習 (ML) 解決方案的普及，對用於模型訓練的大型高品質資料集的需求顯著成長。企業面臨 GDPR 和 CCPA 等嚴格的隱私法規，這些法規限制了對敏感真實世界的存取。合成資料產生透過提供真實、符合隱私法規且保留統計特性的資料集來彌補這一缺口。此外，它還支援在不違反法規的前提下進行可擴展的實驗、測試和演算法改進。醫療保健、金融和自動駕駛系統公司也越來越依賴合成資料集來加速創新，同時確保合規性。

對合成數據的品質和真實性的擔憂

儘管合成數據具有諸多優勢，但與真實世界數據相比，其品質和保真度常常受到嚴格審查。如果合成資料集無法準確地重現統計分佈、極端情況和相關性，那麼基於這些資料集訓練的人工智慧/機器學習模型可能會表現不佳或出現偏差。此外，確保資料在各種應用中的有效性需要先進的生成技術和專業知識，這會增加成本和複雜性。

數據敏感型產業的應用日益普及

在隱私、安全和合規性限制導致無法存取真實資料集的行業中，合成資料蘊藏著巨大的商業機會。醫療保健、銀行、保險和國防等行業可以利用合成資料集來訓練人工智慧模型，而無需洩露個人或敏感資訊。此外，合成數據也擴大被用於測試自動駕駛汽車、機器人和物聯網系統，因為在這些領域收集真實數據成本高且風險巨大。不僅如此，企業也擴大利用合成資料進行場景模擬、演算法檢驗和資料增強，這為那些提供針對高度監管環境量身定做的強大解決方案的供應商創造了新的收入來源。

來自新型資料解決方案（例如資料市場）的競爭

合成數據提供者面臨其他數據採集解決方案的競爭壓力，例如商業數據市場、聯邦學習框架和匿名資料集。這些替代方案能夠以更低的成本和更簡單的部署方式，提供現成的或協作式的真實世界資料存取。此外，企業可能認為市場資料集在某些分析或模型訓練方面更可靠，這限制了其對合成資料的使用。而且，隱私保護人工智慧領域的新興技術，例如同態加密和差分隱私，可能會進一步降低對合成資料集的依賴，從而形成一個對市場成長構成挑戰的競爭格局。

新冠疫情的影響：

新冠疫情加速了數位化技術和遠距辦公的普及，凸顯了在人工智慧/機器學習開發中獲取可存取且符合隱私規定的資料集的重要性。封鎖和限制措施使得現實世界的資料收集面臨挑戰，尤其是在醫療保健和旅行領域。這些情況導致人們更加依賴合成資料進行模型訓練、模擬和預測分析。此外，隨著企業在遵守隱私法律的前提下優先考慮資料主導的決策，合成資料生成解決方案的使用也日益增加。因此，疫情加速了各產業對合成數據技術的廣泛認知、應用與投資。

預計在預測期內，部分合成資料部分將佔比最大。

預計在預測期內，部分合成資料細分市場將佔據最大的市場佔有率。此細分市場融合了真實數據和合成數據，在保障隱私和合規性的同時，降低了完全合成資料集所帶來的風險。企業可從中受益，例如模型效能提升、偏差降低以及部署週期加快。此外，部分合成資料集正日益應用於研究、測試和企業分析等領域，進一步鞏固了其市場主導地位。供應商在產生演算法、檢驗工具和產業特定解決方案方面的投入，也進一步推動了該細分市場的普及，確保其繼續佔據合成資料產生市場的最大佔有率。

預計服務業在預測期內將實現最高的複合年成長率。

預計在預測期內，服務領域將呈現最高的成長率。人工智慧/機器學習（AI/ML）的廣泛應用，以及產生高品質、特定領域合成資料集的複雜性，正在推動對專業服務的需求。此外，企業越來越傾向於採用託管和訂閱模式，以降低營運成本和技術風險。能夠提供從資料生成到檢驗和整合的端到端支援的供應商，將更有利於把握新的商機。此外，隨著人們對監管合規性和模型準確性的認知不斷提高，服務在加速技術應用方面發揮關鍵作用，使其成為合成資料生成市場中成長最快的部分。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率。該地區受益於人工智慧/機器學習技術的廣泛應用、強大的研發基礎設施、早期技術部署以及對隱私合規解決方案的大量投資。此外，主要供應商、新興企業和領先研究機構的存在正在推動合成數據生成領域的創新。諸如HIPAA和CCPA等法律規範正在推動對隱私保護資料集的需求，尤其是在醫療保健、金融和國防領域。此外，高雲端採用率、先進的IT基礎設施和充足的企業預算正在促進合成數據解決方案的快速普及，從而鞏固北美在全球市場的主導地位。

複合年成長率最高的地區：

預計亞太地區在預測期內將呈現最高的複合年成長率。快速的數位轉型、人工智慧/機器學習技術的日益普及、雲端基礎設施的興起以及政府的支持性政策正在推動該地區的成長。此外，不斷擴張的工業和醫療保健產業正在投資符合隱私保護規定的資料解決方案，而新興企業和本地供應商則提供經濟高效的合成資訊服務。智慧型手機普及率、網路存取和數位素養的提高進一步推動了這些技術的普及。此外，跨國公司在該地區的存在也為合作創造了機會，並促進了競爭性成長。這些因素共同推動了亞太地區成為快速成長的市場。

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訂閱本報告的用戶可從以下免費自訂選項中選擇一項：

公司簡介
- 對最多三家其他公司進行全面分析
- 對主要企業進行SWOT分析（最多3家公司）
區域分類
- 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
競爭基準化分析
- 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 德國
- 英國
- 義大利
- 法國
- 西班牙
- 其他歐洲
亞太地區
- 日本
- 中國
- 印度
- 澳洲
- 紐西蘭
- 韓國
- 亞太其他地區
南美洲
- 阿根廷
- 巴西
- 智利
- 其他南美洲
中東和非洲
- 沙烏地阿拉伯
- 阿拉伯聯合大公國
- 卡達
- 南非
- 其他中東和非洲地區

第13章：主要趨勢

合約、商業夥伴關係和合資企業
企業合併（M&A）
新產品發布
業務拓展
其他關鍵策略

第14章：公司簡介

Amazon.com, Inc.
Mostly AI
Synthesis AI
Gretel.ai
Tonic.ai
Meta Platforms, Inc.
Microsoft Corporation
NVIDIA Corporation
OpenAI
Datagen Technologies
CVEDIA Inc.
IBM Corporation
Databricks Inc.
Sogeti（Capgemini Group）
Synthesia Ltd.

簡介目錄圖表

Product Code: SMRC31893

According to Stratistics MRC, the Global Synthetic Data Generation Market is accounted for $0.62 billion in 2025 and is expected to reach $7.93 billion by 2032 growing at a CAGR of 43.9% during the forecast period. Synthetic data generation produces artificial datasets that mirror statistical properties of real data while protecting privacy, enabling AI training, testing, and analytics without using sensitive production records. It helps alleviate labeling scarcity, reduce bias, and accelerate model iteration across regulated sectors. Growth is propelled by AI/ML uptake, privacy regulation, and demand for diverse, large labeled datasets.

Market Dynamics:

Driver:

Rising demand for data for AI/ML training amidst privacy regulations

The growing adoption of artificial intelligence (AI) and machine learning (ML) solutions has significantly increased the need for large, high-quality datasets for model training. Organizations face strict privacy regulations such as GDPR and CCPA, which limit access to real-world sensitive data. Synthetic data generation addresses this gap by providing realistic, privacy-compliant datasets that preserve statistical properties. Furthermore, it enables scalable experimentation, testing, and algorithm improvement without breaching regulations. Additionally, enterprises across healthcare, finance, and autonomous systems increasingly rely on synthetic datasets to accelerate innovation while maintaining compliance.

Restraint:

Concerns about synthetic data quality and fidelity

Despite its advantages, synthetic data is often scrutinized for its quality and fidelity compared to real-world data. If synthetic datasets fail to accurately replicate statistical distributions, edge cases, or correlations, AI/ML models trained on them may underperform or exhibit bias. Moreover, ensuring data validity across diverse applications requires sophisticated generation techniques and domain expertise, increasing cost and complexity.

Opportunity:

Growing adoption in data-sensitive industries

Synthetic data presents significant opportunities in industries where privacy, security, and compliance constraints restrict access to real datasets. Sectors such as healthcare, banking, insurance, and defense can leverage synthetic datasets to train AI models without exposing personal or classified information. Furthermore, adoption is expanding for testing autonomous vehicles, robotics, and IoT systems, where real-world data collection is costly or hazardous. Additionally, enterprises increasingly use synthetic data for scenario simulation, algorithm validation, and data augmentation, unlocking new revenue streams for vendors offering robust, customizable solutions tailored to highly regulated environments.

Threat:

Competition from emerging data solutions like data marketplaces

Synthetic data providers face competitive pressure from alternative data acquisition solutions, such as commercial data marketplaces, federated learning frameworks, and anonymized datasets. These alternatives offer ready-made or collaborative access to real-world data, sometimes at lower costs or with simpler implementation. Moreover, organizations may perceive marketplace datasets as more reliable for certain analytics or model training, limiting synthetic data uptake. Additionally, emerging technologies in privacy-preserving AI, like homomorphic encryption or differential privacy, could further reduce reliance on synthetic datasets, creating a competitive landscape that challenges market growth.

Covid-19 Impact:

The Covid-19 pandemic accelerated the adoption of digital technologies and remote operations, highlighting the importance of accessible, privacy-compliant datasets for AI/ML development. Lockdowns and restrictions made real-world data collection challenging, particularly in healthcare and mobility sectors. This situation increased reliance on synthetic data for model training, simulation, and predictive analytics. Additionally, organizations prioritized data-driven decision-making while adhering to privacy laws, which strengthened the use of synthetic data generation solutions. Consequently, the pandemic acted as a catalyst for broader awareness, adoption, and investment in synthetic data technologies across multiple industries.

The partially synthetic data segment is expected to be the largest during the forecast period

The partially synthetic data segment is expected to account for the largest market share during the forecast period. By offering a blend of real and synthetic data, this segment mitigates risks associated with fully synthetic datasets while maintaining privacy and regulatory compliance. Organizations benefit from enhanced model performance, reduced bias, and accelerated deployment cycles. Additionally, partially synthetic datasets are increasingly adopted for research, testing, and enterprise analytics applications, reinforcing their dominance. Vendor investments in generation algorithms, validation tools, and industry-specific solutions further strengthen adoption, ensuring this segment continues to capture the largest share of the synthetic data generation market.

The services segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the services segment is predicted to witness the highest growth rate. The surge in AI/ML adoption, combined with the complexity of generating high-quality, domain-specific synthetic datasets, fuels demand for specialized services. Additionally, organizations increasingly prefer managed or subscription-based models that reduce operational overhead and technical risks. Vendors offering end-to-end support from data generation to validation and integration are better positioned to capture emerging opportunities. Furthermore, as awareness of regulatory compliance and model accuracy grows, services play a critical role in accelerating adoption, making this segment the fastest-growing component of the synthetic data generation market.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share. The region benefits from strong AI/ML adoption, robust R&D infrastructure, early technology deployment, and substantial investment in privacy-compliant solutions. Additionally, the presence of major vendors, startups, and leading research institutions fosters innovation in synthetic data generation. Regulatory frameworks such as HIPAA and CCPA drive demand for privacy-preserving datasets, particularly in healthcare, finance, and defense sectors. Furthermore, high cloud penetration, advanced IT infrastructure, and strong enterprise budgets enable rapid implementation of synthetic data solutions, sustaining North America's dominant market position globally.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. Rapid digital transformation, increasing AI/ML adoption, rising cloud infrastructure, and supportive government initiatives drive regional growth. Additionally, expanding industrial and healthcare sectors are investing in privacy-compliant data solutions, while startups and local vendors offer cost-effective synthetic data services. Increasing smartphone penetration, internet access, and digital literacy further facilitate adoption. Moreover, multinational corporations entering the region create collaboration opportunities, fueling competitive growth. Collectively, these factors contribute to Asia Pacific emerging as the fastest-growing market.

Key players in the market

Some of the key players in Synthetic Data Generation Market include Amazon.com, Inc., Mostly AI, Synthesis AI, Gretel.ai, Tonic.ai, Meta Platforms, Inc., Microsoft Corporation, NVIDIA Corporation, OpenAI, Datagen Technologies, CVEDIA Inc., IBM Corporation, Databricks Inc., Sogeti (Capgemini Group), and Synthesia Ltd.

Key Developments:

In August 2025, AWS enhanced its Amazon Bedrock generative AI service with new foundational models, improved data processing, prompt caching to reduce costs and latency, and intelligent prompt routing for optimized AI task handling. AWS is also advancing its Knowledge Bases for richer AI applications by enabling structured data retrieval and graph modeling integration, useful for synthetic data applications. These tools are aimed at improving synthetic data use and inference efficiency in AI workloads.

In June 2024, NVIDIA announced Nemotron-4 340B, a family of open models that developers can use to generate synthetic data for training large language models (LLMs) for commercial applications across healthcare, finance, manufacturing, retail and every other industry.

Offerings Covered:

Fully Synthetic Data
Partially Synthetic Data
Hybrid Synthetic Data

Components Covered:

Solution (Software/Platform)
Services

Data Types Covered:

Tabular Data (Structured)
Image & Video Data (Unstructured)
Text Data (Unstructured)
Other Data Types

Modeling Types Covered:

Generative Adversarial Networks (GANs)
Variational Autoencoders (VAEs)
Statistical Methods
Agent-Based Modeling (ABM)
Transformer Models

Deployment Modes Covered:

Cloud-based
On-premise

Applications Covered:

AI/ML Model Training & Development
Test Data Management (TDM) and Quality Assurance (QA)
Data Analytics & Visualization
Enterprise Data Sharing & Monetization
Privacy Protection & Compliance

End Users Covered:

Banking, Financial Services, and Insurance (BFSI)
Healthcare & Life Sciences
Automotive & Transportation
IT & Telecommunication
Retail & E-commerce
Government & Defense
Manufacturing & Industrial
Media & Entertainment
Other End Users

Regions Covered:

North America
- US
- Canada
- Mexico
Europe
- Germany
- UK
- Italy
- France
- Spain
- Rest of Europe
Asia Pacific
- Japan
- China
- India
- Australia
- New Zealand
- South Korea
- Rest of Asia Pacific
South America
- Argentina
- Brazil
- Chile
- Rest of South America
Middle East & Africa
- Saudi Arabia
- UAE
- Qatar
- South Africa
- 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
- Comprehensive profiling of additional market players (up to 3)
- SWOT Analysis of key players (up to 3)
Regional Segmentation
- Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
Competitive Benchmarking
- Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

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 Application Analysis
3.7 End User 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 Generation Market, By Offering

5.1 Introduction
5.2 Fully Synthetic Data
5.3 Partially Synthetic Data
5.4 Hybrid Synthetic Data

6 Global Synthetic Data Generation Market, By Component

6.1 Introduction
6.2 Solution (Software/Platform)
- 6.2.1 AI-Based Generation Platforms
- 6.2.2 Simulation Software
- 6.2.3 Data Masking and Anonymization Tools
- 6.2.4 APIs and Integration Modules
6.3 Services
- 6.3.1 Professional Services
- 6.3.2 Managed Services

7 Global Synthetic Data Generation Market, By Data Type

7.1 Introduction
7.2 Tabular Data (Structured)
- 7.2.1 Time-Series Data
- 7.2.2 Relational/Transactional Data
7.3 Image & Video Data (Unstructured)
7.4 Text Data (Unstructured)
7.5 Other Data Types

8 Global Synthetic Data Generation Market, By Modeling Type

8.1 Introduction
8.2 Generative Adversarial Networks (GANs)
8.3 Variational Autoencoders (VAEs)
8.4 Statistical Methods
8.5 Agent-Based Modeling (ABM)
8.6 Transformer Models

9 Global Synthetic Data Generation Market, By Deployment Mode

9.1 Introduction
9.2 Cloud-based
9.3 On-premise

10 Global Synthetic Data Generation Market, By Application

10.1 Introduction
10.2 AI/ML Model Training & Development
10.3 Test Data Management (TDM) and Quality Assurance (QA)
10.4 Data Analytics & Visualization
10.5 Enterprise Data Sharing & Monetization
10.6 Privacy Protection & Compliance

11 Global Synthetic Data Generation Market, By End User

11.1 Introduction
11.2 Banking, Financial Services, and Insurance (BFSI)
11.3 Healthcare & Life Sciences
11.4 Automotive & Transportation
11.5 IT & Telecommunication
11.6 Retail & E-commerce
11.7 Government & Defense
11.8 Manufacturing & Industrial
11.9 Media & Entertainment
11.10 Other End Users

12 Global Synthetic Data Generation Market, By Geography

12.1 Introduction
12.2 North America
- 12.2.1 US
- 12.2.2 Canada
- 12.2.3 Mexico
12.3 Europe
- 12.3.1 Germany
- 12.3.2 UK
- 12.3.3 Italy
- 12.3.4 France
- 12.3.5 Spain
- 12.3.6 Rest of Europe
12.4 Asia Pacific
- 12.4.1 Japan
- 12.4.2 China
- 12.4.3 India
- 12.4.4 Australia
- 12.4.5 New Zealand
- 12.4.6 South Korea
- 12.4.7 Rest of Asia Pacific
12.5 South America
- 12.5.1 Argentina
- 12.5.2 Brazil
- 12.5.3 Chile
- 12.5.4 Rest of South America
12.6 Middle East & Africa
- 12.6.1 Saudi Arabia
- 12.6.2 UAE
- 12.6.3 Qatar
- 12.6.4 South Africa
- 12.6.5 Rest of Middle East & Africa

13 Key Developments

13.1 Agreements, Partnerships, Collaborations and Joint Ventures
13.2 Acquisitions & Mergers
13.3 New Product Launch
13.4 Expansions
13.5 Other Key Strategies

14 Company Profiling

14.1 Amazon.com, Inc.
14.2 Mostly AI
14.3 Synthesis AI
14.4 Gretel.ai
14.5 Tonic.ai
14.6 Meta Platforms, Inc.
14.7 Microsoft Corporation
14.8 NVIDIA Corporation
14.9 OpenAI
14.10 Datagen Technologies
14.11 CVEDIA Inc.
14.12 IBM Corporation
14.13 Databricks Inc.
14.14 Sogeti (Capgemini Group)
14.15 Synthesia Ltd.

簡介目錄圖表

List of Tables

Table 1 Global Synthetic Data Generation Market Outlook, By Region (2024-2032) ($MN)
Table 2 Global Synthetic Data Generation Market Outlook, By Offering (2024-2032) ($MN)
Table 3 Global Synthetic Data Generation Market Outlook, By Fully Synthetic Data (2024-2032) ($MN)
Table 4 Global Synthetic Data Generation Market Outlook, By Partially Synthetic Data (2024-2032) ($MN)
Table 5 Global Synthetic Data Generation Market Outlook, By Hybrid Synthetic Data (2024-2032) ($MN)
Table 6 Global Synthetic Data Generation Market Outlook, By Component (2024-2032) ($MN)
Table 7 Global Synthetic Data Generation Market Outlook, By Solution (Software/Platform) (2024-2032) ($MN)
Table 8 Global Synthetic Data Generation Market Outlook, By AI-Based Generation Platforms (2024-2032) ($MN)
Table 9 Global Synthetic Data Generation Market Outlook, By Simulation Software (2024-2032) ($MN)
Table 10 Global Synthetic Data Generation Market Outlook, By Data Masking and Anonymization Tools (2024-2032) ($MN)
Table 11 Global Synthetic Data Generation Market Outlook, By APIs and Integration Modules (2024-2032) ($MN)
Table 12 Global Synthetic Data Generation Market Outlook, By Services (2024-2032) ($MN)
Table 13 Global Synthetic Data Generation Market Outlook, By Professional Services (2024-2032) ($MN)
Table 14 Global Synthetic Data Generation Market Outlook, By Managed Services (2024-2032) ($MN)
Table 15 Global Synthetic Data Generation Market Outlook, By Data Type (2024-2032) ($MN)
Table 16 Global Synthetic Data Generation Market Outlook, By Tabular Data (Structured) (2024-2032) ($MN)
Table 17 Global Synthetic Data Generation Market Outlook, By Time-Series Data (2024-2032) ($MN)
Table 18 Global Synthetic Data Generation Market Outlook, By Relational/Transactional Data (2024-2032) ($MN)
Table 19 Global Synthetic Data Generation Market Outlook, By Image & Video Data (Unstructured) (2024-2032) ($MN)
Table 20 Global Synthetic Data Generation Market Outlook, By Text Data (Unstructured) (2024-2032) ($MN)
Table 21 Global Synthetic Data Generation Market Outlook, By Other Data Types (2024-2032) ($MN)
Table 22 Global Synthetic Data Generation Market Outlook, By Modeling Type (2024-2032) ($MN)
Table 23 Global Synthetic Data Generation Market Outlook, By Generative Adversarial Networks (GANs) (2024-2032) ($MN)
Table 24 Global Synthetic Data Generation Market Outlook, By Variational Autoencoders (VAEs) (2024-2032) ($MN)
Table 25 Global Synthetic Data Generation Market Outlook, By Statistical Methods (2024-2032) ($MN)
Table 26 Global Synthetic Data Generation Market Outlook, By Agent-Based Modeling (ABM) (2024-2032) ($MN)
Table 27 Global Synthetic Data Generation Market Outlook, By Transformer Models (2024-2032) ($MN)
Table 28 Global Synthetic Data Generation Market Outlook, By Deployment Mode (2024-2032) ($MN)
Table 29 Global Synthetic Data Generation Market Outlook, By Cloud-based (2024-2032) ($MN)
Table 30 Global Synthetic Data Generation Market Outlook, By On-premise (2024-2032) ($MN)
Table 31 Global Synthetic Data Generation Market Outlook, By Application (2024-2032) ($MN)
Table 32 Global Synthetic Data Generation Market Outlook, By AI/ML Model Training & Development (2024-2032) ($MN)
Table 33 Global Synthetic Data Generation Market Outlook, By Test Data Management (TDM) and Quality Assurance (QA) (2024-2032) ($MN)
Table 34 Global Synthetic Data Generation Market Outlook, By Data Analytics & Visualization (2024-2032) ($MN)
Table 35 Global Synthetic Data Generation Market Outlook, By Enterprise Data Sharing & Monetization (2024-2032) ($MN)
Table 36 Global Synthetic Data Generation Market Outlook, By Privacy Protection & Compliance (2024-2032) ($MN)
Table 37 Global Synthetic Data Generation Market Outlook, By End User (2024-2032) ($MN)
Table 38 Global Synthetic Data Generation Market Outlook, By Banking, Financial Services, and Insurance (BFSI) (2024-2032) ($MN)
Table 39 Global Synthetic Data Generation Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
Table 40 Global Synthetic Data Generation Market Outlook, By Automotive & Transportation (2024-2032) ($MN)
Table 41 Global Synthetic Data Generation Market Outlook, By IT & Telecommunication (2024-2032) ($MN)
Table 42 Global Synthetic Data Generation Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
Table 43 Global Synthetic Data Generation Market Outlook, By Government & Defense (2024-2032) ($MN)
Table 44 Global Synthetic Data Generation Market Outlook, By Manufacturing & Industrial (2024-2032) ($MN)
Table 45 Global Synthetic Data Generation Market Outlook, By Media & Entertainment (2024-2032) ($MN)
Table 46 Global Synthetic Data Generation Market Outlook, By Other End Users (2024-2032) ($MN)