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Global Synthetic Data Generation Market to reach USD 1344.26 million by the end of 2029

Global Synthetic Data Generation Market Size study & Forecast, by Data Type (Tabular Data, Text Data, Image & Video Data, Others), by Modeling Type (Direct Modeling, Agent-based Modeling), by Application (Data Protection, Predictive Analytics, Natural Language Processing, Computer Vision Algorithms, Others), by End User (BFSI, Healthcare & Life sciences, Transportation & Logistics, IT & Telecommunication, Retail & E-commerce, Others) and Regional Analysis, 2022-2029

Product Code: ICTNGT-65411053
Publish Date: 19-12-2022
Page: 200

Global Synthetic Data Generation Market is valued at approximately USD 123.3 million in 2021 and is anticipated to grow with a healthy growth rate of more than 34.8 % over the forecast period 2022-2029. Synthetic data generation is a technique of creating artificial that is gaining high traction in place of real data to train AI models. Synthetic data is generated algorithmically and is used to train machine learning models, validate mathematical models, and act as a stand-in for test datasets of production or operational data. The growing need to maintain data security and privacy, coupled with the rising inclination toward synthetic data to train machine learning, anti-money laundering behaviours and payment data for fraud detection are the prominent factors for the market growth.

The rapid proliferation of Artificial Intelligence (AI) is fostering the adoption of the synthetic generation of data in the global market. According to Statista, the global artificial intelligence (AI) software market generated around USD 10.1 billion in terms of sales in 2018, and it is projected to reach about USD 126 billion by 2025. Likewise, in August 2020, the White House reported unveiled the total investment of USD 1 billion in artificial intelligence and quantum computing. Thereby, the high usage of data is influencing the demand for connected devices and IoT, which, in turn, propels the demand for synthetic data to create on-demand data. Moreover, the growing investment in advanced technologies, as well as increasing initiatives by key market players are presenting various opportunities over the forecasting years. However, the rising acceptance of data accuracy is hindering the market growth throughout the forecast period of 2022-2029.

The key regions considered for the Global Synthetic Data Generation Market study include Asia Pacific, North America, Europe, Latin America, and the Rest of the World. North America dominated the market in terms of revenue, owing to the growing focus on increased inclination toward fraud detection and the rising presence of the leading industry players such as American Express, Google’s Waymo J.P. Morgan, and Amazon. Whereas, the Asia Pacific is expected to grow at the highest growth rate over the forecasting period. Factors such as rising acceptance of emerging technologies, as well as the exponential growth of various end-use industries, are burgeoning the market growth in the forecasting years.

Major market players included in this report are:
Mostly AI
Synthesis AI
Statice
YData
Ekobit d.o.o.
Hazy
Kinetic Vision, Inc.
Kymera-labs
MDClone
Neuromation
Recent Developments in the Market:
 In April 2022, Synthesis AI publicized that the company raised USD 17 million in Series A to make synthetic data for computer vision AI, carrying the total funding to above USD 24 million.
 In October 2021, Facebook announced the acquisition of AI. Reverie- a leading synthetic data platform, which aims on helping businesses to improve and scale their machine learning algorithms by creating synthetic data.

Global Synthetic Data Generation Market Report Scope:
Historical Data 2019-2020-2021
Base Year for Estimation 2021
Forecast period 2022-2029
Report Coverage Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Segments Covered Data Type, Modeling Type, Application, End User, Region
Regional Scope North America; Europe; Asia Pacific; Latin America; Rest of the World
Customization Scope Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:
By Data Type:
Tabular Data
Text Data
Image & Video Data
Others
By Modeling Type:
Direct Modeling
Agent-based Modeling
By Application:
Data Protection
Predictive Analytics
Natural Language Processing
Computer Vision Algorithms
Others
By End User:
BFSI
Healthcare & Life sciences
Transportation & Logistics
IT & Telecommunication
Retail & E-commerce
Others

By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE
Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2029 (USD Million)
1.2.1. Synthetic Data Generation Market, by Region, 2019-2029 (USD Million)
1.2.2. Synthetic Data Generation Market, by Data Type, 2019-2029 (USD Million)
1.2.3. Synthetic Data Generation Market, by Modeling Type, 2019-2029 (USD Million)
1.2.4. Synthetic Data Generation Market, by Application, 2019-2029 (USD Million)
1.2.5. Synthetic Data Generation Market, by End User, 2019-2029 (USD Million)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Synthetic Data Generation Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Synthetic Data Generation Market Dynamics
3.1. Synthetic Data Generation Market Impact Analysis (2019-2029)
3.1.1. Market Drivers
3.1.1.1. Growing need to maintain data security and privacy
3.1.1.2. Rapid proliferation of Artificial Intelligence (AI)
3.1.2. Market Challenges
3.1.2.1. Rising acceptance for data accuracy
3.1.3. Market Opportunities
3.1.3.1. Growing investment in advanced technologies
3.1.3.2. Increasing initiatives by key market players
Chapter 4. Global Synthetic Data Generation Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Futuristic Approach to Porter’s 5 Force Model (2019-2029)
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.4. Investment Adoption Model
4.5. Analyst Recommendation & Conclusion
4.6. Top investment opportunity
4.7. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1. Assessment of the overall impact of COVID-19 on the industry
5.2. Pre COVID-19 and post COVID-19 Market scenario
Chapter 6. Global Synthetic Data Generation Market, by Data Type
6.1. Market Snapshot
6.2. Global Synthetic Data Generation Market by Data Type, Performance – Potential Analysis
6.3. Global Synthetic Data Generation Market Estimates & Forecasts by Data Type 2019-2029 (USD Million)
6.4. Synthetic Data Generation Market, Sub Segment Analysis
6.4.1. Tabular Data
6.4.2. Text Data
6.4.3. Image & Video Data
6.4.4. Others
Chapter 7. Global Synthetic Data Generation Market, by Modeling Type
7.1. Market Snapshot
7.2. Global Synthetic Data Generation Market by Modeling Type, Performance – Potential Analysis
7.3. Global Synthetic Data Generation Market Estimates & Forecasts by Modeling Type 2019-2029 (USD Million)
7.4. Synthetic Data Generation Market, Sub Segment Analysis
7.4.1. Direct Modeling
7.4.2. Agent-based Modeling
Chapter 8. Global Synthetic Data Generation Market, by Application
8.1. Market Snapshot
8.2. Global Synthetic Data Generation Market by Application, Performance – Potential Analysis
8.3. Global Synthetic Data Generation Market Estimates & Forecasts by Application 2019-2029 (USD Million)
8.4. Synthetic Data Generation Market, Sub Segment Analysis
8.4.1. Data Protection
8.4.2. Predictive Analytics
8.4.3. Natural Language Processing
8.4.4. Computer Vision Algorithms
8.4.5. Others
Chapter 9. Global Synthetic Data Generation Market, by End User
9.1. Market Snapshot
9.2. Global Synthetic Data Generation Market by End User, Performance – Potential Analysis
9.3. Global Synthetic Data Generation Market Estimates & Forecasts by End User 2019-2029 (USD Million)
9.4. Synthetic Data Generation Market, Sub Segment Analysis
9.4.1. BFSI
9.4.2. Healthcare & Life sciences
9.4.3. Transportation & Logistics
9.4.4. IT & Telecommunication
9.4.5. Retail & E-commerce
9.4.6. Others
Chapter 10. Global Synthetic Data Generation Market, Regional Analysis
10.1. Synthetic Data Generation Market, Regional Market Snapshot
10.2. North America Synthetic Data Generation Market
10.2.1. U.S. Synthetic Data Generation Market
10.2.1.1. Data Type breakdown estimates & forecasts, 2019-2029
10.2.1.2. Modeling Type breakdown estimates & forecasts, 2019-2029
10.2.1.3. Application breakdown estimates & forecasts, 2019-2029
10.2.1.4. End User breakdown estimates & forecasts, 2019-2029
10.2.2. Canada Synthetic Data Generation Market
10.3. Europe Synthetic Data Generation Market Snapshot
10.3.1. U.K. Synthetic Data Generation Market
10.3.2. Germany Synthetic Data Generation Market
10.3.3. France Synthetic Data Generation Market
10.3.4. Spain Synthetic Data Generation Market
10.3.5. Italy Synthetic Data Generation Market
10.3.6. Rest of Europe Synthetic Data Generation Market
10.4. Asia-Pacific Synthetic Data Generation Market Snapshot
10.4.1. China Synthetic Data Generation Market
10.4.2. India Synthetic Data Generation Market
10.4.3. Japan Synthetic Data Generation Market
10.4.4. Australia Synthetic Data Generation Market
10.4.5. South Korea Synthetic Data Generation Market
10.4.6. Rest of Asia Pacific Synthetic Data Generation Market
10.5. Latin America Synthetic Data Generation Market Snapshot
10.5.1. Brazil Synthetic Data Generation Market
10.5.2. Mexico Synthetic Data Generation Market
10.6. Rest of The World Synthetic Data Generation Market

Chapter 11. Competitive Intelligence
11.1. Top Market Strategies
11.2. Company Profiles
11.2.1. Mostly AI
11.2.1.1. Key Information
11.2.1.2. Overview
11.2.1.3. Financial (Subject to Data Availability)
11.2.1.4. Product Summary
11.2.1.5. Recent Developments
11.2.2. Synthesis AI
11.2.3. Statice
11.2.4. YData
11.2.5. Ekobit d.o.o.
11.2.6. Hazy
11.2.7. Kinetic Vision, Inc.
11.2.8. Kymera-labs
11.2.9. MDClone
11.2.10. Neuromation
Chapter 12. Research Process
12.1. Research Process
12.1.1. Data Mining
12.1.2. Analysis
12.1.3. Market Estimation
12.1.4. Validation
12.1.5. Publishing
12.2. Research Attributes
12.3. Research Assumption

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Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
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Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
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