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市场调查报告书
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1857028

全球合成资料生成市场：未来预测（至2032年）－依产品/服务、元件、资料类型、建模类型、部署方法、应用、最终使用者和地区进行分析

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: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，预计 2025 年全球合成数据生成市场规模将达到 6.2 亿美元，到 2032 年将达到 79.3 亿美元，预测期内复合年增长率将达到 43.9%。

合成资料生成技术能够产生与真实资料统计特性相符的人工资料集，同时保护使用者隐私，从而在无需敏感生产记录的情况下实现人工智慧的训练、测试和分析。这有助于缓解标註数据稀缺的问题，减少数据偏差，并加速受监管行业的模型迭代。人工智慧/机器学习的广泛应用、隐私法规的规范以及对多样化、大规模标註资料集的需求，共同推动了合成资料生成技术的发展。

在隐私法规的背景下，对人工智慧/机器学习训练资料的需求不断增长。

随着人工智慧 (AI) 和机器学习 (ML) 解决方案的普及，对用于模型训练的大型高品质资料集的需求显着增长。企业面临 GDPR 和 CCPA 等严格的隐私法规，这些法规限制了对敏感真实世界的存取。合成资料产生透过提供真实、符合隐私法规且保留统计特性的资料集来弥补这一缺口。此外，它还支援在不违反法规的前提下进行可扩展的实验、测试和演算法改进。医疗保健、金融和自动驾驶系统公司也越来越依赖合成资料集来加速创新，同时确保合规性。

对合成数据的品质和真实性的担忧

儘管合成数据具有诸多优势，但与真实世界数据相比，其品质和保真度常常受到严格审查。如果合成资料集无法准确地重现统计分布、极端情况和相关性，那么基于这些资料集训练的人工智慧/机器学习模型可能会表现不佳或出现偏差。此外，确保资料在各种应用中的有效性需要先进的生成技术和专业知识，这会增加成本和复杂性。

数据敏感型产业的应用日益普及

在隐私、安全和合规性限制导致无法存取真实资料集的行业中，合成资料蕴藏着巨大的商业机会。医疗保健、银行、保险和国防等行业可以利用合成资料集来训练人工智慧模型，而无需洩露个人或敏感资讯。此外，合成数据也越来越多地被用于测试自动驾驶汽车、机器人和物联网系统，因为在这些领域收集真实数据成本高且风险巨大。不仅如此，企业也越来越多地利用合成资料进行场景模拟、演算法检验和资料增强，这为那些提供针对高度监管环境量身定制的强大解决方案的供应商创造了新的收入来源。

来自新型资料解决方案（例如资料市场）的竞争

合成数据提供者面临其他数据采集解决方案的竞争压力，例如商业数据市场、联邦学习框架和匿名数据集。这些替代方案能够以更低的成本和更简单的部署方式，提供现成的或协作式的真实世界资料存取。此外，企业可能认为市场资料集在某些分析或模型训练方面更可靠，这限制了其对合成资料的使用。而且，隐私保护人工智慧领域的新兴技术，例如同态加密和差分隐私，可能会进一步降低对合成资料集的依赖，从而形成一个对市场成长构成挑战的竞争格局。

新冠疫情的影响：

新冠疫情加速了数位化技术和远距办公的普及，凸显了在人工智慧/机器学习开发中获取可存取且符合隐私规定的资料集的重要性。封锁和限制措施使得现实世界的资料收集面临挑战，尤其是在医疗保健和旅行领域。这些情况导致人们更加依赖合成资料进行模型训练、模拟和预测分析。此外，随着企业在遵守隐私法律的前提下优先考虑资料主导的决策，合成资料生成解决方案的使用也日益增加。因此，疫情加速了各产业对合成数据技术的广泛认知、应用与投资。

预计在预测期内，部分合成资料部分将占比最大。

预计在预测期内，部分合成资料细分市场将占据最大的市场份额。此细分市场融合了真实数据和合成数据，在保障隐私和合规性的同时，降低了完全合成数据集所带来的风险。企业可从中受益，例如模型效能提升、偏差降低以及部署週期加快。此外，部分合成资料集正日益应用于研究、测试和企业分析等领域，进一步巩固了其市场主导地位。供应商在产生演算法、检验工具和产业特定解决方案方面的投入，也进一步推动了该细分市场的普及，确保其继续占据合成资料产生市场的最大份额。

预计服务业在预测期内将实现最高的复合年增长率。

预计在预测期内，服务领域将呈现最高的成长率。人工智慧/机器学习（AI/ML）的广泛应用，以及产生高品质、特定领域合成资料集的复杂性，正在推动对专业服务的需求。此外，企业越来越倾向于采用託管和订阅模式，以降低营运成本和技术风险。能够提供从资料生成到检验和整合的端到端支援的供应商，将更有利于把握新的商机。此外，随着人们对监管合规性和模型准确性的认识不断提高，服务在加速技术应用方面发挥关键作用，使其成为合成资料生成市场中成长最快的部分。

占比最大的地区：

预计北美将在预测期内占据最大的市场份额。该地区受益于人工智慧/机器学习技术的广泛应用、强大的研发基础设施、早期技术部署以及对隐私合规解决方案的大量投资。此外，主要供应商、新兴企业和领先研究机构的存在正在推动合成数据生成领域的创新。诸如HIPAA和CCPA等法律规范正在推动对隐私保护资料集的需求，尤其是在医疗保健、金融和国防领域。此外，高云端采用率、先进的IT基础设施和充足的企业预算正在促进合成数据解决方案的快速普及，从而巩固北美在全球市场的主导地位。

复合年增长率最高的地区：

预计亚太地区在预测期内将呈现最高的复合年增长率。快速的数位转型、人工智慧/机器学习技术的日益普及、云端基础设施的兴起以及政府的支持性政策正在推动该地区的成长。此外，不断扩张的工业和医疗保健产业正在投资符合隐私保护规定的资料解决方案，而新兴企业和本地供应商则提供经济高效的合成资讯服务。智慧型手机普及率、网路存取和数位素养的提高进一步推动了这些技术的普及。此外，跨国公司在该地区的存在也为合作创造了机会，并促进了竞争性成长。这些因素共同推动了亚太地区成为快速成长的市场。

免费客製化服务

订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球合成资料生成市场（依产品/服务划分）

  • 完全合成数据
  • 部分合成数据
  • 混合合成数据

6. 全球合成资料生成市场（按组件划分）

  • 解决方案（软体/平台）
    • 基于人工智慧的生成平台
    • 模拟软体
    • 资料遮罩与匿名化工具
    • API 和整合模组
  • 服务
    • 专业服务
    • 託管服务

7. 全球合成资料生成市场（依资料类型划分）

  • 表格形式资料（结构化）
    • 时间序列数据
    • 关係型/事务型数据
  • 影像和影片资料（非结构化）
  • 文字资料（非结构化）
  • 其他资料类型

8. 全球合成资料生成市场（依建模类型划分）

  • 生成对抗网路（GAN）
  • 变分自编码器（VAE）
  • 统计方法
  • 基于代理的建模（ABM）
  • 变压器模型

9. 全球合成资料生成市场依部署方式划分

  • 云端基础的
  • 本地部署

第十章 全球合成资料生成市场（依应用领域划分）

  • AI/ML模型训练与开发
  • 测试资料管理（TDM）和品质保证（QA）
  • 数据分析与视觉化
  • 共用和利用企业数据获利
  • 隐私保护与合规

第十一章 全球合成资料生成市场（依最终用户划分）

  • 银行、金融服务和保险（BFSI）
  • 医学与生命科​​学
  • 汽车与运输
  • 资讯科技/通讯
  • 零售与电子商务
  • 政府/国防
  • 製造业/工业
  • 媒体与娱乐
  • 其他最终用户

12. 全球合成资料生成市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十三章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十四章：公司简介

  • 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.
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

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 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.

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)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.