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1438084

到 2030 年的綜合資料產生市場預測：按組件、部署模式、產品、建模類型、資料類型、應用程式、最終用戶和區域進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，2023 年全球合成資料生成市場規模為 3.7245 億美元，預計到 2030 年將達到 22.2616 億美元，預測期內年複合成長率為 29.1%。

創建與現實世界資料的統計特徵和模式非常相似但沒有任何個人識別資訊的人工資料集的過程稱為合成資料生成。此步驟在機器學習等各個領域特別有用，在這些領域中，存取大型且多樣化的資料集對於測試和訓練模型至關重要。

美國醫學會表示，實施全面的醫療保健政策對於確保公平獲得優質醫療保健服務並滿足不同人口患者的多樣化需求至關重要。

對多樣化訓練資料集的需求不斷增加

各行業機器學習應用的指數級成長推動了對廣泛且多樣化的資料集的需求，以學習可靠且準確的模型。此外，合成資料產生可以滿足這一需求，合成資料產生提供了一種可擴展的方式來產生不同的資料集，從而更容易使機器學習演算法的訓練過程更加成功和高效。

缺乏衡量標準和標準

由於缺乏創建和分析合成資料的既定程序，因此很難確定人工創建的資料集的有效性和品質。此外，必須建立普遍認可的評估標準來評估合成資料的有效性和可靠性，並確保不同行業和應用的透明和統一的實踐。

針對特定使用案例的個人化

為特定使用案例客製合成資料產生是一個重要的機會。如果合成資料集的設計更接近特定產業、應用或研究領域，則可以更有效地訓練和測試機器學習模型。此外，這提供了僅靠真實世界資料難以實現的特異性程度。

代表性不足和偏誤放大

無法捕捉現實世界資料的真正多樣性和複雜性對合成資料的創建構成了嚴重威脅。如果不仔細設計，合成資料集可能會引入偏差或無法捕捉感興趣領域中發現的某些細微差別。此外，這可能會導致模型不能很好地概括，甚至強化現有的偏差。

COVID-19 的影響

由於對需求和營運動態的影響，COVID-19 大流行對合成資料產生市場產生了重大影響。一方面，對遠距工作和數位轉型的日益關注正在推動對合成資料等最尖端科技的需求，以支援遠端位置的機器學習開發。然而，由於預算限制和經濟不確定性，一些組織正在重新考慮其投資，這可能會減緩市場成長。疫情造成的產業混亂也凸顯了在現實世界資料不可用或不切實際的情況下合成資料的價值。

預測分析產業預計將在預測期內成為最大的產業

預計預測分析領域將在預測期內佔據最大的市場佔有率。使用統計演算法、機器學習技術以及歷史和當前資料，預測分析可以幫助企業透過發現模式和趨勢來預測未來事件和結果。此外，這個市場在行銷、電子商務、金融和醫療保健等許多領域越來越受歡迎，越來越多的參考資料表明公司根據資料主導的見解做出主動決策的好處。這是因為

預計 BFSI 細分市場在預測期內年複合成長率最高

預計年複合成長率最高的行業是 BFSI（銀行、金融服務和保險）行業。由於 BFSI 行業在共用敏感的財務和資料資料測試和開發方面遇到了困難，合成資料正在成為模型訓練和檢驗的重要解決方案。此外，BFSI 的應用包括風險評估、詐騙偵測和合規性測試。合成資料促進創新，同時確保遵守資料隱私法規。

比最大的地區

預計北美將佔據最大的市場佔有率。最尖端科技的早期採用、主要行業參與者的強大影響力以及機器學習和人工智慧應用的先進生態系統的發展是該地區優勢的因素。此外，美國的合成資料市場正在顯著成長，因為合成資料被用於開發、測試和訓練技術、醫療保健、金融和汽車等領域的模型。

年複合成長率最高的地區

亞太地區預計將見證合成資料生成市場最高的年複合成長率。合成資料需求的強勁成長部分是由於人工智慧投資的增加、新興技術的快速採用以及該地區技術主導產業的不斷成長。此外，中國、印度、日本和韓國等國家在醫療保健、金融、製造和零售等行業的應用不斷增加，為合成資料解決方案創造了有利的環境。

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  • 公司簡介
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    • 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
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 調查來源
    • 主要調查來源
    • 二次調查來源
    • 先決條件

第3章市場趨勢分析

  • 促進因素
  • 抑制因素
  • 機會
  • 威脅
  • 應用分析
  • 最終用戶分析
  • 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
  • 買方議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間存在敵對關係

第5章全球綜合資料生成市場：按組成部分

  • 解決方案/平台
  • 服務
  • 其他組件

第6章全球綜合資料生成市場：依部署模式

  • 本地
  • 雲

第7章全球綜合資料生成市場：透過提供

  • 完全合成的資料
  • 部分綜合資料
  • 混合合成資料
  • 其他產品

第8章全球綜合資料生成市場：依建模類型

  • 直接建模
  • 基於代理的建模
  • 其他建模類型

第9章全球綜合資料生成市場：依資料類型

  • 表格形式資料
  • 文字資料
  • 影像和視訊資料
  • 其他資料類型

第10章全球綜合資料生成市場：依應用分類

  • 資料保護
  • 資料共用
  • 預測分析
  • 自然語言處理
  • 電腦視覺演算法
  • 其他用途

第 11 章 全球綜合資料產生市場：依最終使用者分類

  • BFSI
  • 醫療保健和生命科學
  • 零售與電子商務
  • 汽車和交通
  • 政府和國防
  • 資訊科技與資訊科技服務
  • 製造業
  • 其他最終用戶

第12章全球綜合資料生成市場：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
    • 南美洲其他地區
  • 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

第13章 主要進展

  • 合約、夥伴關係、協作和合資企業
  • 收購和合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第14章 公司簡介

  • 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.