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市场调查报告书
商品编码
1803106

2032 年合成资料市场预测：按类型、资料形态、部署、技术、应用和地区进行的全球分析

Synthetic Data Market 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
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，全球合成数据市场预计在 2025 年达到 4.198 亿美元，到 2032 年将达到 34.664 亿美元，预测期内的复合年增长率为 35.2%。

合成资料是人工生成的讯息，它复製了真实世界资料的统计属性和结构，但不会洩露敏感资讯。合成资料使用演算法、模拟和生成模型创建，模拟了真实世界资料集中的模式、变异性和复杂性。它被广泛用于训练人工智慧系统、测试软体以及在资料共用过程中保护隐私。与匿名资料不同，合成资料集是从零开始建构的，既确保了分析的效用，又能防范与个人资料相关的风险。

据 Gartner 称，合成资料的采用正在加速，预计到 2027 年 60% 的人工智慧主导企业将使用合成资料进行模型训练。

人工智慧培训需求不断成长

随着企业和研究机构越来越需要大量且多样化的资料集来优化机器学习模型，人工智慧训练需求的不断增长正在显着影响合成资料市场。合成资料对于深度学习应用极为宝贵，因为它能够在不损害隐私的情况下提供可扩展性。在自动化、数位转型以及对先进人工智慧模型日益增长的依赖的推动下，企业正在利用合成资料集来模拟复杂的现实场景，提高模型准确性，并简化人工智慧开发中的创新。

缺乏跨产业标准化

各行业缺乏标准化，阻碍了合成数据的采用，因为各组织在互通性、检验和合规性框架方面举步维艰。缺乏统一的基准，人们持续担忧人工生成资料集的可靠性和可比性。受碎片化采用模式的影响，许多公司不愿将合成资料完全整合到关键应用程式中。因此，不一致的品质保证和缺乏全球通讯协定构成了重大障碍，限制了市场扩张，并减缓了金融、医疗保健和製造等领域对合成资料集的主流接受度。

扩展到医疗保健AI应用

由于医院和研究机构需要安全、匿名的资料集进行模型训练，医疗AI应用领域的扩展为合成资料市场带来了诱人的成长机会。在严格的患者资料隐私法规的推动下，合成资料集为诊断演算法、个人化医疗和临床模拟的开发提供了解决方案。在精准医疗和法规合规性需求日益增长的推动下，合成数据提供者正越来越多地与医疗机构合作，以加速AI的普及、降低风险并促进医疗技术创新。

与匿名真实资料集的竞争

来自匿名现实世界资料集的竞争对合成资料的采用构成了重大威胁，因为许多组织仍然偏爱传统的匿名化方法，因为它们经济高效且为人所知。多年来，由于监管部门的认可，匿名资料集通常被认为足以满足非敏感使用案例，这对合成资料提供者构成了挑战。然而，匿名数据存在被重新识别的风险。儘管如此，其成熟的应用和较低的整合门槛创造了一个竞争格局，在这个格局中，合成资料解决方案必须持续展现出卓越的安全性、可扩展性和可靠性。

COVID-19的影响：

新冠疫情加速了数位化，推动了对安全、可扩展的合成资料集的需求，这些资料集用于模拟资料中断并支援人工智慧主导的决策。远距办公和线上医疗咨询需要安全的数据处理，这进一步增强了合成数据的采用。疫情期间，基于人工智慧的预测模型的激增也推动了成长，企业利用合成资料集进行医疗保健研究、增强供应链韧性和检测诈欺。因此，疫情如同催化剂，再形成了市场格局，凸显了对隐私保护型大规模合成资料解决方案的需求。

预计全合成数据部分将在预测期内成为最大的部分

预计全合成资料领域将在预测期内占据最大市场占有率，这得益于其能够产生完全人工的资料集，从而消除隐私顾虑。与部分合成方法不同，全合成资料能够确保医疗保健、金融和零售等产业获得更高的保护，并具备更强的适应性。它能够反映真实数据的统计特征，同时保持合规性标准，因此极具吸引力，尤其是在需要严格隐私保护措施的监管主导行业。

影像和影片资料部分预计将在预测期内实现最高的复合年增长率

受电脑视觉、自动驾驶汽车和扩增实境应用快速扩张的推动，影像和影像资料领域预计将在预测期内实现最高成长率。合成影像资料集使人工智慧模型无需数百万张真实世界图像和影像即可进行训练。在监控、医疗影像和零售分析需求日益增长的推动下，该领域正经历前所未有的普及。其在复製真实世界复杂性方面的多功能性，正在推动多个行业强劲发展。

最大份额区域：

预计亚太地区将在预测期内占据最大的市场占有率，这得益于快速扩张的数位生态系统、不断增长的人工智慧投资以及大规模的企业应用。中国、印度和日本等国家在製造业、金融业和智慧城市领域采用基于人工智慧的创新方面处于领先地位。政府对人工智慧研究的支持以及数据本地化政策使亚太地区成为强大的市场领导者，为合成数据的扩张创造了有利环境。

复合年增长率最高的地区：

在预测期内，北美预计将实现最高的复合年增长率，这得益于其先进的人工智慧研究生态系统、强大的合成数据新兴企业以及日益加强的数据隐私监管力度。在科技巨头、学术机构和医疗创新者之间的合作推动下，北美正见证各行各业的强劲应用。早期采用尖端人工智慧模型以及强劲的创业投资资金，使该地区成为快速成长的合成数据创新中心。

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  • 公司简介
    • 全面分析其他市场参与者（最多 3 家公司）
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  • 区域细分
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  • 竞争基准化分析
    • 根据产品系列、地理分布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 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 二手研究资料
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球合成资料市场类型

  • 完全合成数据
  • 部分合成数据
  • 混合合成数据
  • 匿名合成数据
  • 其他类型

6. 全球合成资料市场（依资料形态划分）

  • 表格形式数据
  • 文字资料（NLP 和聊天机器人）
  • 影像和影片数据
  • 音讯数据
  • 时间序列数据
  • 多模态数据

7. 全球合成资料市场（按部署）

  • 云端基础的解决方案
  • 本地解决方案
  • 混合部署

8. 全球合成数据市场（按技术）

  • 生成对抗网路（GAN）
  • 基于代理的模型
  • 基于变压器的模型
  • 其他技术

9. 全球合成数据市场（按应用）

  • 训练和测试模型
  • 增强资料隐私和安全
  • 诈骗侦测和风险管理
  • 医疗保健和基因组学研究
  • 自治系统
  • 其他用途

第十章全球合成资料市场（按地区）

  • 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
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美
  • 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

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章 公司概况

  • Mostly AI
  • Synthesis AI
  • Gretel.ai
  • Hazy
  • Cognitensor
  • MDClone
  • AI.Reverie
  • Datagen Technologies
  • Zebracat AI
  • Statice
  • Tonic.ai
  • Cauliflower
  • Sky Engine AI
  • Informatica
  • Microsoft
  • IBM Research
Product Code: SMRC30631

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
    • 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 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

  • Table 1 Global Synthetic Data Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Synthetic Data Market Outlook, By Type (2024-2032) ($MN)
  • Table 3 Global Synthetic Data Market Outlook, By Fully Synthetic Data (2024-2032) ($MN)
  • Table 4 Global Synthetic Data Market Outlook, By Partially Synthetic Data (2024-2032) ($MN)
  • Table 5 Global Synthetic Data Market Outlook, By Hybrid Synthetic Data (2024-2032) ($MN)
  • Table 6 Global Synthetic Data Market Outlook, By Anonymized Synthetic Data (2024-2032) ($MN)
  • Table 7 Global Synthetic Data Market Outlook, By Other Types (2024-2032) ($MN)
  • Table 8 Global Synthetic Data Market Outlook, By Data Modality (2024-2032) ($MN)
  • Table 9 Global Synthetic Data Market Outlook, By Tabular Data (2024-2032) ($MN)
  • Table 10 Global Synthetic Data Market Outlook, By Text Data (NLP & Chatbots) (2024-2032) ($MN)
  • Table 11 Global Synthetic Data Market Outlook, By Image & Video Data (2024-2032) ($MN)
  • Table 12 Global Synthetic Data Market Outlook, By Audio Data (2024-2032) ($MN)
  • Table 13 Global Synthetic Data Market Outlook, By Time-Series Data (2024-2032) ($MN)
  • Table 14 Global Synthetic Data Market Outlook, By Multi-Modal Data (2024-2032) ($MN)
  • Table 15 Global Synthetic Data Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 16 Global Synthetic Data Market Outlook, By Cloud-Based Solutions (2024-2032) ($MN)
  • Table 17 Global Synthetic Data Market Outlook, By On-Premises Solutions (2024-2032) ($MN)
  • Table 18 Global Synthetic Data Market Outlook, By Hybrid Deployment (2024-2032) ($MN)
  • Table 19 Global Synthetic Data Market Outlook, By Technology (2024-2032) ($MN)
  • Table 20 Global Synthetic Data Market Outlook, By Generative Adversarial Networks (GANs) (2024-2032) ($MN)
  • Table 21 Global Synthetic Data Market Outlook, By Agent-Based Models (2024-2032) ($MN)
  • Table 22 Global Synthetic Data Market Outlook, By Transformer-Based Models (2024-2032) ($MN)
  • Table 23 Global Synthetic Data Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 24 Global Synthetic Data Market Outlook, By Application (2024-2032) ($MN)
  • Table 25 Global Synthetic Data Market Outlook, By Model Training & Testing (2024-2032) ($MN)
  • Table 26 Global Synthetic Data Market Outlook, By Data Privacy & Security Enhancement (2024-2032) ($MN)
  • Table 27 Global Synthetic Data Market Outlook, By Fraud Detection & Risk Management (2024-2032) ($MN)
  • Table 28 Global Synthetic Data Market Outlook, By Healthcare & Genomics Research (2024-2032) ($MN)
  • Table 29 Global Synthetic Data Market Outlook, By Autonomous Systems (2024-2032) ($MN)
  • Table 30 Global Synthetic Data Market Outlook, By Other Applications (2024-2032) ($MN)

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