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市场调查报告书
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1438084

到 2030 年的综合资料产生市场预测：按组件、部署模式、产品、建模类型、资料类型、应用程式、最终用户和区域进行的全球分析

Synthetic Data Generation Market Forecasts to 2030 - Global Analysis By Component, Deployment Mode, Offering, Modeling Type, Data Type, 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，2023 年全球合成资料生成市场规模为 3.7245 亿美元，预计到 2030 年将达到 22.2616 亿美元，预测期内复合年增长率为 29.1%。

创建与现实世界资料的统计特征和模式非常相似但没有任何个人识别资讯的人工资料集的过程称为合成资料生成。此步骤在机器学习等各个领域特别有用，在这些领域中，存取大型且多样化的资料集对于测试和训练模型至关重要。

美国医学会表示，实施全面的医疗保健政策对于确保公平获得优质医疗保健服务并满足不同人口患者的多样化需求至关重要。

对多样化训练资料集的需求不断增加

各行业机器学习应用的指数级增长推动了对广泛且多样化的资料集的需求，以学习可靠且准确的模型。此外，合成资料产生可以满足这一需求，合成资料产生提供了一种可扩展的方式来产生不同的资料集，从而更容易使机器学习演算法的训练过程更加成功和高效。

缺乏衡量标准和标准

由于缺乏创建和分析合成资料的既定程序，因此很难确定人工创建的资料集的有效性和品质。此外，必须建立普遍认可的评估标准来评估合成资料的有效性和可靠性，并确保不同行业和应用的透明和统一的实践。

针对特定使用案例的个人化

为特定使用案例客製合成资料产生是一个重要的机会。如果合成资料集的设计更接近特定产业、应用或研究领域，则可以更有效地训练和测试机器学习模型。此外，这提供了仅靠真实世界资料难以实现的特异性程度。

代表性不足和偏误放大

无法捕捉现实世界资料的真正多样性和复杂性对合成资料的创建构成了严重威胁。如果不仔细设计，合成资料集可能会引入偏差或无法捕捉感兴趣领域中发现的某些细微差别。此外，这可能会导致模型不能很好地概括，甚至强化现有的偏差。

COVID-19 的影响

由于对需求和营运动态的影响，COVID-19 大流行对合成资料产生市场产生了重大影响。一方面，对远距工作和数位转型的日益关注正在推动对合成资料等最尖端科技的需求，以支援远端位置的机器学习开发。然而，由于预算限制和经济不确定性，一些组织正在重新考虑其投资，这可能会减缓市场成长。疫情造成的产业混乱也凸显了在现实世界资料不可用或不切实际的情况下合成资料的价值。

预测分析产业预计将在预测期内成为最大的产业

预计预测分析领域将在预测期内占据最大的市场占有率。使用统计演算法、机器学习技术以及历史和当前资料，预测分析可以帮助企业透过发现模式和趋势来预测未来事件和结果。此外，这个市场在行销、电子商务、金融和医疗保健等许多领域越来越受欢迎，越来越多的参考资料表明公司根据资料主导的见解做出主动决策的好处。这是因为

预计 BFSI 细分市场在预测期内复合年增长率最高

预计复合年增长率最高的行业是 BFSI（银行、金融服务和保险）行业。由于 BFSI 行业在共用敏感的财务和资料资料测试和开发方面遇到了困难，合成资料正在成为模型训练和检验的重要解决方案。此外，BFSI 的应用包括风险评估、诈骗侦测和合规性测试。合成资料促进创新，同时确保遵守资料隐私法规。

比最大的地区

预计北美将占据最大的市场占有率。最尖端科技的早期采用、主要行业参与者的强大影响力以及机器学习和人工智慧应用的先进生态系统的发展是该地区优势的因素。此外，美国的合成资料市场正在显着增长，因为合成资料被用于开发、测试和训练技术、医疗保健、金融和汽车等领域的模型。

复合年增长率最高的地区

亚太地区预计将见证合成资料生成市场最高的复合年增长率。合成资料需求的强劲成长部分是由于人工智慧投资的增加、新兴技术的快速采用以及该地区技术主导产业的不断增长。此外，中国、印度、日本和韩国等国家在医疗保健、金融、製造和零售等行业的应用不断增加，为合成资料解决方案创造了有利的环境。

免费客製化服务

订阅此报告的客户可以存取以下免费自订选项之一：

  • 公司简介
    • 其他市场参与者的综合分析（最多 3 家公司）
    • 主要企业SWOT分析（最多3家企业）
  • 区域分割
    • 根据客户兴趣对主要国家的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 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
    • 资料分析
    • 资料检验
    • 研究途径
  • 调查来源
    • 主要调查来源
    • 二次调查来源
    • 先决条件

第三章市场趋势分析

  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19 的影响

第4章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间存在敌对关係

第五章全球综合资料生成市场：按组成部分

  • 解决方案/平台
  • 服务
  • 其他组件

第六章全球综合资料生成市场：依部署模式

  • 本地
  • 云

第七章全球综合资料生成市场：透过提供

  • 完全合成的资料
  • 部分综合资料
  • 混合合成资料
  • 其他产品

第八章全球综合资料生成市场：依建模类型

  • 直接建模
  • 基于代理的建模
  • 其他建模类型

第九章全球综合资料生成市场：依资料类型

  • 表格形式资料
  • 文字资料
  • 影像和视讯资料
  • 其他资料类型

第十章全球综合资料生成市场：依应用分类

  • 资料保护
  • 资料共用
  • 预测分析
  • 自然语言处理
  • 电脑视觉演算法
  • 其他用途

第 11 章 全球综合资料产生市场：依最终使用者分类

  • BFSI
  • 医疗保健和生命科学
  • 零售与电子商务
  • 汽车和交通
  • 政府和国防
  • 资讯科技与资讯科技服务
  • 製造业
  • 其他最终用户

第十二章全球综合资料生成市场：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
    • 南美洲其他地区
  • 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

第十三章 主要进展

  • 合约、伙伴关係、协作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十四章 公司简介

  • IBM
  • Google
  • AWS
  • TonicAI, Inc
  • Hazy Limited
  • Microsoft
  • Gretel Labs, Inc
  • Replica Analytics Ltd
  • Datagen
  • Informatica
  • GenRocket, Inc
  • YData Labs Inc
  • TCS
  • Replica Analytics Ltd
Product Code: SMRC25072

According to Stratistics MRC, the Global Synthetic Data Generation Market is accounted for $372.45 million in 2023 and is expected to reach $2226.16 million by 2030 growing at a CAGR of 29.1% during the forecast period. The process of creating artificial datasets devoid of any personally identifiable information that closely resembles the statistical traits and patterns of real-world data is known as synthetic data generation. This procedure is especially helpful in a variety of domains, like machine learning, where having access to sizable and varied datasets is essential for testing and training models.

According to the American Medical Association, implementing comprehensive healthcare policies is essential for ensuring equitable access to quality medical services and addressing the diverse needs of patients across different demographic groups.

Market Dynamics:

Driver:

Growing requirement for various training datasets

The demand for broad and varied datasets to train reliable and accurate models has increased due to the exponential rise in machine learning applications across industries. Additionally, this need is met by synthetic data generation, which offers a scalable way to produce diverse datasets, facilitating more successful and efficient machine learning algorithm training procedures.

Restraint:

Absence of evaluation metrics and standards

The lack of established procedures for creating and analyzing synthetic data makes it difficult to judge the appropriateness and caliber of datasets that have been created artificially. Furthermore, it is imperative to establish metrics that are universally recognized in order to assess the efficacy and dependability of synthetic data and guarantee transparent and uniform practices across various industries and applications.

Opportunity:

Personalization for particular use cases

The customization of synthetic data generation for particular use cases represents a significant opportunity. More efficient training and testing of machine learning models is possible when synthetic datasets are designed to closely resemble specific industries, applications, or research domains. Moreover, this provides a level of specificity that may be difficult to attain with real-world data alone.

Threat:

Insufficient representativeness and amplification of bias

The potential inadequacy of capturing the true diversity and complexity of real-world data poses a serious threat to the creation of synthetic data. Synthetic datasets can introduce biases or fail to capture particular nuances found in the target domain if they are not carefully designed. Additionally, this can result in models that do not generalize well and can even reinforce preexisting biases.

Covid-19 Impact:

Due to its impact on demand and operational dynamics, the COVID-19 pandemic has had a major effect on the synthetic data generation market. On the one hand, the demand for cutting-edge technologies, such as synthetic data, to support machine learning development remotely has increased due to the growing emphasis on remote work and digital transformation. However, some organizations have re-evaluated their investments due to budgetary constraints and economic uncertainties, which may slow down market growth. Industry disruptions caused by the pandemic have also highlighted the value of synthetic data in situations where real-world data is either unobtainable or impractical.

The Predictive Analytics segment is expected to be the largest during the forecast period

During the projected period, the predictive analytics segment is expected to hold the largest market share. With the use of statistical algorithms, machine learning techniques, and historical and current data, predictive analytics helps businesses anticipate future events and outcomes by spotting patterns and trends. Furthermore, this market has grown in popularity in a number of sectors, such as marketing, e-commerce, finance, and healthcare, as companies learn more and more about the benefits of making proactive decisions based on data-driven insights.

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

The industry's highest CAGR is anticipated for the BFSI (banking, financial services, and insurance) sector. Synthetic data is becoming a more vital solution for model training and validation as the BFSI industry struggles to share sensitive financial and customer data for testing and development. Additionally, applications in BFSI include risk assessment, fraud detection, and compliance testing. Synthetic data promotes innovation while guaranteeing adherence to data privacy regulations.

Region with largest share:

It is projected that North America will command the largest market share. The early adoption of cutting-edge technologies, the robust presence of major industry players, and the development of an advanced ecosystem for machine learning and artificial intelligence applications are all factors contributing to the region's dominance. Moreover, in large part due to the use of synthetic data for model development, testing, and training by sectors including technology, healthcare, finance, and automotive, the synthetic data market has grown significantly in the United States.

Region with highest CAGR:

In the market for synthetic data generation, Asia-Pacific is anticipated to have the highest CAGR. The robust growth in demand for synthetic data is partly explained by the region's increasing investments in artificial intelligence, rapid adoption of emerging technologies, and growing presence of tech-driven industries. Furthermore, applications in industries including healthcare, finance, manufacturing, and retail are increasing in nations like China, India, Japan, and South Korea, creating a good environment for synthetic data solutions.

Key players in the market

Some of the key players in Synthetic Data Generation market include IBM, Google, AWS, TonicAI, Inc, Hazy Limited, Microsoft, Gretel Labs, Inc, Replica Analytics Ltd, Datagen, Informatica, GenRocket, Inc, YData Labs Inc, TCS and Replica Analytics Ltd.

Key Developments:

In January 2024, Google India Digital Services and NPCI International Payments (NIPL), a wholly-owned subsidiary of the National Payments Corporation of India (NPCI) have signed a Memorandum of Understanding (MoU) to enable UPI transactions outside India. The MoU seeks to broaden the use of UPI payments for Indian travellers to make transactions abroad. It also aims to establish UPI-like digital payment systems in other countries, providing a model for seamless financial transactions.

In January 2024, Amazon Web Services (AWS) looks set to make more money on three multi-million pound government contracts that went live on the same day in December 2023 than it has previously amassed through its decade-long involvement with the G-Cloud procurement framework. The public cloud giant signed three 36-month contracts with several different major government departments that all went live on 1 December 2023, including one valued at £350m with HM Revenue and Customs and another worth £94m with the Department for Work and Pensions.

In January 2024, Microsoft and Vodafone announced a significant 10-year strategic partnership aimed at driving digital transformation for businesses and consumers across Europe and Africa, leveraging their combined strengths in technology and connectivity. The collaboration will focus on enhancing Vodafone's customer experience through Microsoft's AI, expanding Vodafone's managed IoT connectivity platform, developing new digital and financial services for SMEs, and revamping Vodafone's global data center strategy.

Components Covered:

  • Solution/Platform
  • Services
  • Other Components

Deployment Modes Covered:

  • On-Premise
  • Cloud

Offerings Covered:

  • Fully Synthetic Data
  • Partially Synthetic Data
  • Hybrid Synthetic Data
  • Other Offerings

Modeling Types Covered:

  • Direct Modeling
  • Agent-based Modeling
  • Other Modeling Types

Data Types Covered:

  • Tabular Data
  • Text data
  • Image and Video Data
  • Other Data Types

Applications Covered:

  • Data Protection
  • Data Sharing
  • Predictive Analytics
  • Natural Language Processing
  • Computer Vision Algorithms
  • Other Applications

End Users Covered:

  • BFSI
  • Healthcare & Life sciences
  • Retail and E-commerce
  • Automotive and Transportation
  • Government & Defense
  • IT and ITeS
  • Manufacturing
  • 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 2021, 2022, 2023, 2026, and 2030
  • 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 Component

  • 5.1 Introduction
  • 5.2 Solution/Platform
  • 5.3 Services
  • 5.4 Other Components

6 Global Synthetic Data Generation Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premise
  • 6.3 Cloud

7 Global Synthetic Data Generation Market, By Offering

  • 7.1 Introduction
  • 7.2 Fully Synthetic Data
  • 7.3 Partially Synthetic Data
  • 7.4 Hybrid Synthetic Data
  • 7.5 Other Offerings

8 Global Synthetic Data Generation Market, By Modeling Type

  • 8.1 Introduction
  • 8.2 Direct Modeling
  • 8.3 Agent-based Modeling
  • 8.4 Other Modeling Types

9 Global Synthetic Data Generation Market, By Data Type

  • 9.1 Introduction
  • 9.2 Tabular Data
  • 9.3 Text data
  • 9.4 Image and Video Data
  • 9.5 Other Data Types

10 Global Synthetic Data Generation Market, By Application

  • 10.1 Introduction
  • 10.2 Data Protection
  • 10.3 Data Sharing
  • 10.4 Predictive Analytics
  • 10.5 Natural Language Processing
  • 10.6 Computer Vision Algorithms
  • 10.7 Other Applications

11 Global Synthetic Data Generation Market, By End User

  • 11.1 Introduction
  • 11.2 BFSI
  • 11.3 Healthcare & Life sciences
  • 11.4 Retail and E-commerce
  • 11.5 Automotive and Transportation
  • 11.6 Government & Defense
  • 11.7 IT and ITeS
  • 11.8 Manufacturing
  • 11.9 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 IBM
  • 14.2 Google
  • 14.3 AWS
  • 14.4 TonicAI, Inc
  • 14.5 Hazy Limited
  • 14.6 Microsoft
  • 14.7 Gretel Labs, Inc
  • 14.8 Replica Analytics Ltd
  • 14.9 Datagen
  • 14.10 Informatica
  • 14.11 GenRocket, Inc
  • 14.12 YData Labs Inc
  • 14.13 TCS
  • 14.14 Replica Analytics Ltd

List of Tables

  • Table 1 Global Synthetic Data Generation Market Outlook, By Region (2021-2030) ($MN)
  • Table 2 Global Synthetic Data Generation Market Outlook, By Component (2021-2030) ($MN)
  • Table 3 Global Synthetic Data Generation Market Outlook, By Solution/Platform (2021-2030) ($MN)
  • Table 4 Global Synthetic Data Generation Market Outlook, By Services (2021-2030) ($MN)
  • Table 5 Global Synthetic Data Generation Market Outlook, By Other Components (2021-2030) ($MN)
  • Table 6 Global Synthetic Data Generation Market Outlook, By Deployment Mode (2021-2030) ($MN)
  • Table 7 Global Synthetic Data Generation Market Outlook, By On-Premise (2021-2030) ($MN)
  • Table 8 Global Synthetic Data Generation Market Outlook, By Cloud (2021-2030) ($MN)
  • Table 9 Global Synthetic Data Generation Market Outlook, By Offering (2021-2030) ($MN)
  • Table 10 Global Synthetic Data Generation Market Outlook, By Fully Synthetic Data (2021-2030) ($MN)
  • Table 11 Global Synthetic Data Generation Market Outlook, By Partially Synthetic Data (2021-2030) ($MN)
  • Table 12 Global Synthetic Data Generation Market Outlook, By Hybrid Synthetic Data (2021-2030) ($MN)
  • Table 13 Global Synthetic Data Generation Market Outlook, By Other Offerings (2021-2030) ($MN)
  • Table 14 Global Synthetic Data Generation Market Outlook, By Modeling Type (2021-2030) ($MN)
  • Table 15 Global Synthetic Data Generation Market Outlook, By Direct Modeling (2021-2030) ($MN)
  • Table 16 Global Synthetic Data Generation Market Outlook, By Agent-based Modeling (2021-2030) ($MN)
  • Table 17 Global Synthetic Data Generation Market Outlook, By Other Modeling Types (2021-2030) ($MN)
  • Table 18 Global Synthetic Data Generation Market Outlook, By Data Type (2021-2030) ($MN)
  • Table 19 Global Synthetic Data Generation Market Outlook, By Tabular Data (2021-2030) ($MN)
  • Table 20 Global Synthetic Data Generation Market Outlook, By Text data (2021-2030) ($MN)
  • Table 21 Global Synthetic Data Generation Market Outlook, By Image and Video Data (2021-2030) ($MN)
  • Table 22 Global Synthetic Data Generation Market Outlook, By Other Data Types (2021-2030) ($MN)
  • Table 23 Global Synthetic Data Generation Market Outlook, By Application (2021-2030) ($MN)
  • Table 24 Global Synthetic Data Generation Market Outlook, By Data Protection (2021-2030) ($MN)
  • Table 25 Global Synthetic Data Generation Market Outlook, By Data Sharing (2021-2030) ($MN)
  • Table 26 Global Synthetic Data Generation Market Outlook, By Predictive Analytics (2021-2030) ($MN)
  • Table 27 Global Synthetic Data Generation Market Outlook, By Natural Language Processing (2021-2030) ($MN)
  • Table 28 Global Synthetic Data Generation Market Outlook, By Computer Vision Algorithms (2021-2030) ($MN)
  • Table 29 Global Synthetic Data Generation Market Outlook, By Other Applications (2021-2030) ($MN)
  • Table 30 Global Synthetic Data Generation Market Outlook, By End User (2021-2030) ($MN)
  • Table 31 Global Synthetic Data Generation Market Outlook, By BFSI (2021-2030) ($MN)
  • Table 32 Global Synthetic Data Generation Market Outlook, By Healthcare & Life sciences (2021-2030) ($MN)
  • Table 33 Global Synthetic Data Generation Market Outlook, By Retail and E-commerce (2021-2030) ($MN)
  • Table 34 Global Synthetic Data Generation Market Outlook, By Automotive and Transportation (2021-2030) ($MN)
  • Table 35 Global Synthetic Data Generation Market Outlook, By Government & Defense (2021-2030) ($MN)
  • Table 36 Global Synthetic Data Generation Market Outlook, By IT and ITeS (2021-2030) ($MN)
  • Table 37 Global Synthetic Data Generation Market Outlook, By Manufacturing (2021-2030) ($MN)
  • Table 38 Global Synthetic Data Generation Market Outlook, By Other End Users (2021-2030) ($MN)

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