Synthetic Data Generation Market
Synthetic Data Generation Market Forecasts to 2030 - Global Analysis By Component (Solution/Platform, Services and Other Components), Deployment Mode (On-Premise and Cloud), Offering (Fully Synthetic Data, Partially Synthetic Data, Hybrid Synthetic Data and Other Offerings), Modeling Type (Direct Modeling, Agent-based Modeling and Other Modeling Types), Data Type, Application, End User and by Geography
|
Years Covered |
2021-2030 |
|
Estimated Year Value (2023) |
US $372.45 MN |
|
Projected Year Value (2030) |
US $2,226.16 MN |
|
CAGR (2023 - 2030) |
29.1% |
|
Regions Covered |
North America, Europe, Asia Pacific, South America, and Middle East & Africa |
|
Countries Covered |
US, Canada, Mexico, Germany, UK, Italy, France, Spain, Japan, China, India, Australia, New Zealand, South Korea, Rest of Asia Pacific, South America, Argentina, Brazil, Chile, Middle East & Africa, Saudi Arabia, UAE, Qatar, and South Africa |
|
Largest Market |
North America |
|
Highest Growing Market |
Asia Pacific |
According to Stratistics MRC, the Global Synthetic Data Generation Market is accounted for $372.45 million in 2023 and is expected to reach $2,226.16 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
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o 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
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o 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
1 Global Synthetic Data Generation Market Outlook, By Region (2021-2030) ($MN)
2 Global Synthetic Data Generation Market Outlook, By Component (2021-2030) ($MN)
3 Global Synthetic Data Generation Market Outlook, By Solution/Platform (2021-2030) ($MN)
4 Global Synthetic Data Generation Market Outlook, By Services (2021-2030) ($MN)
5 Global Synthetic Data Generation Market Outlook, By Other Components (2021-2030) ($MN)
6 Global Synthetic Data Generation Market Outlook, By Deployment Mode (2021-2030) ($MN)
7 Global Synthetic Data Generation Market Outlook, By On-Premise (2021-2030) ($MN)
8 Global Synthetic Data Generation Market Outlook, By Cloud (2021-2030) ($MN)
9 Global Synthetic Data Generation Market Outlook, By Offering (2021-2030) ($MN)
10 Global Synthetic Data Generation Market Outlook, By Fully Synthetic Data (2021-2030) ($MN)
11 Global Synthetic Data Generation Market Outlook, By Partially Synthetic Data (2021-2030) ($MN)
12 Global Synthetic Data Generation Market Outlook, By Hybrid Synthetic Data (2021-2030) ($MN)
13 Global Synthetic Data Generation Market Outlook, By Other Offerings (2021-2030) ($MN)
14 Global Synthetic Data Generation Market Outlook, By Modeling Type (2021-2030) ($MN)
15 Global Synthetic Data Generation Market Outlook, By Direct Modeling (2021-2030) ($MN)
16 Global Synthetic Data Generation Market Outlook, By Agent-based Modeling (2021-2030) ($MN)
17 Global Synthetic Data Generation Market Outlook, By Other Modeling Types (2021-2030) ($MN)
18 Global Synthetic Data Generation Market Outlook, By Data Type (2021-2030) ($MN)
19 Global Synthetic Data Generation Market Outlook, By Tabular Data (2021-2030) ($MN)
20 Global Synthetic Data Generation Market Outlook, By Text data (2021-2030) ($MN)
21 Global Synthetic Data Generation Market Outlook, By Image and Video Data (2021-2030) ($MN)
22 Global Synthetic Data Generation Market Outlook, By Other Data Types (2021-2030) ($MN)
23 Global Synthetic Data Generation Market Outlook, By Application (2021-2030) ($MN)
24 Global Synthetic Data Generation Market Outlook, By Data Protection (2021-2030) ($MN)
25 Global Synthetic Data Generation Market Outlook, By Data Sharing (2021-2030) ($MN)
26 Global Synthetic Data Generation Market Outlook, By Predictive Analytics (2021-2030) ($MN)
27 Global Synthetic Data Generation Market Outlook, By Natural Language Processing (2021-2030) ($MN)
28 Global Synthetic Data Generation Market Outlook, By Computer Vision Algorithms (2021-2030) ($MN)
29 Global Synthetic Data Generation Market Outlook, By Other Applications (2021-2030) ($MN)
30 Global Synthetic Data Generation Market Outlook, By End User (2021-2030) ($MN)
31 Global Synthetic Data Generation Market Outlook, By BFSI (2021-2030) ($MN)
32 Global Synthetic Data Generation Market Outlook, By Healthcare & Life sciences (2021-2030) ($MN)
33 Global Synthetic Data Generation Market Outlook, By Retail and E-commerce (2021-2030) ($MN)
34 Global Synthetic Data Generation Market Outlook, By Automotive and Transportation (2021-2030) ($MN)
35 Global Synthetic Data Generation Market Outlook, By Government & Defense (2021-2030) ($MN)
36 Global Synthetic Data Generation Market Outlook, By IT and ITeS (2021-2030) ($MN)
37 Global Synthetic Data Generation Market Outlook, By Manufacturing (2021-2030) ($MN)
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.
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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