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市場調查報告書
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1803106

2032 年合成資料市場預測：按類型、資料形態、部署、技術、應用和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球合成數據市場預計在 2025 年達到 4.198 億美元，到 2032 年將達到 34.664 億美元，預測期內的複合年成長率為 35.2%。

合成資料是人工生成的訊息，它複製了真實世界資料的統計屬性和結構，但不會洩露敏感資訊。合成資料使用演算法、模擬和生成模型創建，模擬了真實世界資料集中的模式、變異性和複雜性。它被廣泛用於訓練人工智慧系統、測試軟體以及在資料共用過程中保護隱私。與匿名資料不同，合成資料集是從零開始建構的，既確保了分析的效用，又能防範與個人資料相關的風險。

據 Gartner 稱，合成資料的採用正在加速，預計到 2027 年 60% 的人工智慧主導企業將使用合成資料進行模型訓練。

人工智慧培訓需求不斷成長

隨著企業和研究機構越來越需要大量且多樣化的資料集來最佳化機器學習模型，人工智慧訓練需求的不斷成長正在顯著影響合成資料市場。合成資料對於深度學習應用極為寶貴，因為它能夠在不損害隱私的情況下提供可擴展性。在自動化、數位轉型以及對先進人工智慧模型日益成長的依賴的推動下，企業正在利用合成資料集來模擬複雜的現實場景，提高模型準確性，並簡化人工智慧開發中的創新。

缺乏跨產業標準化

各行業缺乏標準化，阻礙了合成數據的採用，因為各組織在互通性、檢驗和合規性框架方面舉步維艱。缺乏統一的基準，人們持續擔憂人工生成資料集的可靠性和可比性。受碎片化採用模式的影響，許多公司不願將合成資料完全整合到關鍵應用程式中。因此，不一致的品質保證和缺乏全球通訊協定構成了重大障礙，限制了市場擴張，並減緩了金融、醫療保健和製造等領域對合成資料集的主流接受度。

擴展到醫療保健AI應用

由於醫院和研究機構需要安全、匿名的資料集進行模型訓練，醫療AI應用領域的擴展為合成資料市場帶來了誘人的成長機會。在嚴格的患者資料隱私法規的推動下，合成資料集為診斷演算法、個人化醫療和臨床模擬的開發提供了解決方案。在精準醫療和法規合規性需求日益成長的推動下，合成數據提供者正擴大與醫療機構合作，以加速AI的普及、降低風險並促進醫療技術創新。

與匿名真實資料集的競爭

來自匿名現實世界資料集的競爭對合成資料的採用構成了重大威脅，因為許多組織仍然偏愛傳統的匿名化方法，因為它們經濟高效且為人所知。多年來，由於監管部門的認可，匿名資料集通常被認為足以滿足非敏感使用案例，這對合成資料提供者構成了挑戰。然而，匿名數據存在被重新識別的風險。儘管如此，其成熟的應用和較低的整合門檻創造了一個競爭格局，在這個格局中，合成資料解決方案必須持續展現出卓越的安全性、可擴展性和可靠性。

COVID-19的影響：

新冠疫情加速了數位化，推動了對安全、可擴展的合成資料集的需求，這些資料集用於模擬資料中斷並支援人工智慧主導的決策。遠距辦公和線上醫療諮詢需要安全的數據處理，這進一步增強了合成數據的採用。疫情期間，基於人工智慧的預測模型的激增也推動了成長，企業利用合成資料集進行醫療保健研究、增強供應鏈韌性和檢測詐欺。因此，疫情如同催化劑，再形成了市場格局，凸顯了對隱私保護型大規模合成資料解決方案的需求。

預計全合成數據部分將在預測期內成為最大的部分

預計全合成資料領域將在預測期內佔據最大市場佔有率，這得益於其能夠產生完全人工的資料集，從而消除隱私顧慮。與部分合成方法不同，全合成資料能夠確保醫療保健、金融和零售等產業獲得更高的保護，並具備更強的適應性。它能夠反映真實數據的統計特徵，同時保持合規性標準，因此極具吸引力，尤其是在需要嚴格隱私保護措施的監管主導行業。

影像和影片資料部分預計將在預測期內實現最高的複合年成長率

受電腦視覺、自動駕駛汽車和擴增實境應用快速擴張的推動，影像和影像資料領域預計將在預測期內實現最高成長率。合成影像資料集使人工智慧模型無需數百萬張真實世界圖像和影像即可進行訓練。在監控、醫療影像和零售分析需求日益成長的推動下，該領域正經歷前所未有的普及。其在複製真實世界複雜性方面的多功能性，正在推動多個行業強勁發展。

最大佔有率區域：

預計亞太地區將在預測期內佔據最大的市場佔有率，這得益於快速擴張的數位生態系統、不斷成長的人工智慧投資以及大規模的企業應用。中國、印度和日本等國家在製造業、金融業和智慧城市領域採用基於人工智慧的創新方面處於領先地位。政府對人工智慧研究的支持以及數據本地化政策使亞太地區成為強大的市場領導者，為合成數據的擴張創造了有利環境。

複合年成長率最高的地區：

在預測期內，北美預計將實現最高的複合年成長率，這得益於其先進的人工智慧研究生態系統、強大的合成數據新興企業以及日益加強的數據隱私監管力度。在科技巨頭、學術機構和醫療創新者之間的合作推動下，北美正見證各行各業的強勁應用。早期採用尖端人工智慧模型以及強勁的創業投資資金，使該地區成為快速成長的合成數據創新中心。

免費客製化服務

此報告的訂閱者可以使用以下免費自訂選項之一：

  • 公司簡介
    • 全面分析其他市場參與者（最多 3 家公司）
    • 主要企業的SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 二手研究資料
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球合成資料市場類型

  • 完全合成數據
  • 部分合成數據
  • 混合合成數據
  • 匿名合成數據
  • 其他類型

6. 全球合成資料市場（依資料形態分類）

  • 表格形式數據
  • 文字資料（NLP 和聊天機器人）
  • 影像和影片數據
  • 音訊數據
  • 時間序列數據
  • 多模態數據

7. 全球合成資料市場（按部署）

  • 雲端基礎的解決方案
  • 本地解決方案
  • 混合部署

8. 全球合成數據市場（按技術）

  • 生成對抗網路（GAN）
  • 基於代理的模型
  • 基於變壓器的模型
  • 其他技術

9. 全球合成數據市場（按應用）

  • 訓練和測試模型
  • 增強資料隱私和安全
  • 詐騙偵測和風險管理
  • 醫療保健和基因組學研究
  • 自治系統
  • 其他用途

第10章全球合成資料市場（按地區）

  • 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
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美
  • 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11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章 公司概況

  • 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.