세계의 합성 데이터 생성 시장 : 데이터 유형별, 컴포넌트별, 모델링별, 제공 제품별, 용도별, 최종 사용자별-예측(2025-2030년)

The Synthetic Data Generation Market was valued at USD 437.20 million in 2023, expected to reach USD 576.02 million in 2024, and is projected to grow at a CAGR of 34.04%, to USD 3,400.23 million by 2030.

Synthetic data generation involves creating artificial datasets that mimic real-world data while maintaining its statistical properties. The scope of synthetic data encompasses its necessity in addressing privacy concerns, reducing the cost associated with data collection, and overcoming the limitations of unavailability or inadequate real data for training machine-learning models. Its application ranges across various domains such as finance, healthcare, and autonomous systems, where synthetic data is used for testing, training, and enhancing algorithms without the risk of exposing sensitive information. The end-use scope includes sectors like AI model development, product testing, risk analysis, and simulation environments.

The market for synthetic data is driven by key growth factors such as increasing data privacy regulations, the demand for high-quality training data for AI and machine learning, and the vast benefits associated with simulated data environments for testing and validation processes. A notable market opportunity lies in industries like autonomous driving, where synthetic data can simulate countless driving scenarios, aiding in safer and more efficient systems. The rise of advanced technologies, such as cloud computing and AI, paves new pathways for innovation in generating scalable and highly realistic datasets. Companies can seize these opportunities by investing in creating robust synthetic data tools that offer customizable, adaptive, and contextually rich datasets.

However, challenges persist, including the need to ensure synthetic data sets are truly representative and free from bias, which can otherwise lead to erroneous model outcomes. Additionally, establishing trust in synthetic data's utility and accuracy remains a hurdle, especially in sectors heavily reliant on data integrity. Innovations for growth should focus on developing sophisticated algorithms capable of generating context-aware, scenario-specific datasets that improve model robustness. The market nature is dynamic, with continued advancements in technology and growing acceptance of synthetic data use across industries. Future research should explore refining data synthesis processes, optimizing data fidelity, and integrating ethical considerations in synthetic data generation practices to ensure widespread adoption and trust.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Synthetic Data Generation Market

The Synthetic Data Generation Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

• Market Drivers
  • Growing importance of data privacy regulations accelerating the adoption of synthetic data
  • Expansion of advanced technologies, including artificial intelligence and machine learning
  • Increase in digitalization transformation across enterprises.
• Market Restraints
  • Concerns associated with data privacy and security breach
• Market Opportunities
  • Rising potential of synthetic data in drug discovery with AI-driven pharmaceutical research
  • Growing application of synthetic data in expanding retail and e-commerce sector
• Market Challenges
  • Lack of skilled workforce and complexity associated with integrating synthetic data solutions with existing data infrastructures

Porter's Five Forces: A Strategic Tool for Navigating the Synthetic Data Generation Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Synthetic Data Generation Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Synthetic Data Generation Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Synthetic Data Generation Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Synthetic Data Generation Market

A detailed market share analysis in the Synthetic Data Generation Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Synthetic Data Generation Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Synthetic Data Generation Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Synthetic Data Generation Market

A strategic analysis of the Synthetic Data Generation Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Synthetic Data Generation Market, highlighting leading vendors and their innovative profiles. These include Amazon Web Services, Inc., ANONOS INC., BetterData Pte Ltd, Broadcom Corporation, Capgemini SE, ChipIn Inc., Datawizz.ai, Folio3 Software Inc., GenRocket, Inc., Gretel Labs, Inc., Hazy Limited, Informatica Inc., International Business Machines Corporation, K2view Ltd., Kroop AI Private Limited, Kymera-labs, MDClone Limited, Microsoft Corporation, MOSTLY AI, SAEC / Kinetic Vision, Inc., Synthesis AI, Inc., Synthesized Ltd., Synthon International Holding B.V., TonicAI, Inc., and YData Labs Inc..

Market Segmentation & Coverage

This research report categorizes the Synthetic Data Generation Market to forecast the revenues and analyze trends in each of the following sub-markets:

• Based on Data Type, market is studied across Image & Video Data, Tabular Data, and Text Data.
• Based on Component, market is studied across Services and Software.
• Based on Modeling, market is studied across Agent-based Modeling and Statistical Distribution Modeling.
• Based on Offering, market is studied across Fully Synthetic Data, Hybrid Synthetic Data, and Partially Synthetic Data.
• Based on Application, market is studied across AI/ML Training & Development, Data Analytics & Visualization, Enterprise Data Sharing, and Test Data Management.
• Based on End-User, market is studied across Automotive, Banking & Finance, Government & Defense, Healthcare & Lifesciences, Logistics & Transportation, Manufacturing, Retail, and Telecommunication & IT.
• Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Growing importance of data privacy regulations accelerating the adoption of synthetic data
    • 5.1.1.2. Expansion of advanced technologies, including artificial intelligence and machine learning
    • 5.1.1.3. Increase in digitalization transformation across enterprises.
  • 5.1.2. Restraints
    • 5.1.2.1. Concerns associated with data privacy and security breach
  • 5.1.3. Opportunities
    • 5.1.3.1. Rising potential of synthetic data in drug discovery with AI-driven pharmaceutical research
    • 5.1.3.2. Growing application of synthetic data in expanding retail and e-commerce sector
  • 5.1.4. Challenges
    • 5.1.4.1. Lack of skilled workforce and complexity associated with integrating synthetic data solutions with existing data infrastructures
• 5.2. Market Segmentation Analysis
  • 5.2.1. Component: Preference for software solutions that offer more flexibility for organizations seeking targeted data generation techniques
  • 5.2.2. Data Type: Expanding usage of tabular data synthesis that focuses on preserving statistical properties
  • 5.2.3. Application: Rising usage for AI/ML training & development which improves decision-making through insightful graphical representations
  • 5.2.4. End-Use: Increasing usage across the government & defense sector due to its ability to address privacy regulation challenges
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Synthetic Data Generation Market, by Data Type

• 6.1. Introduction
• 6.2. Image & Video Data
• 6.3. Tabular Data
• 6.4. Text Data

7. Synthetic Data Generation Market, by Component

• 7.1. Introduction
• 7.2. Services
• 7.3. Software

8. Synthetic Data Generation Market, by Modeling

• 8.1. Introduction
• 8.2. Agent-based Modeling
• 8.3. Statistical Distribution Modeling

9. Synthetic Data Generation Market, by Offering

• 9.1. Introduction
• 9.2. Fully Synthetic Data
• 9.3. Hybrid Synthetic Data
• 9.4. Partially Synthetic Data

10. Synthetic Data Generation Market, by Application

• 10.1. Introduction
• 10.2. AI/ML Training & Development
• 10.3. Data Analytics & Visualization
• 10.4. Enterprise Data Sharing
• 10.5. Test Data Management

11. Synthetic Data Generation Market, by End-User

• 11.1. Introduction
• 11.2. Automotive
• 11.3. Banking & Finance
• 11.4. Government & Defense
• 11.5. Healthcare & Lifesciences
• 11.6. Logistics & Transportation
• 11.7. Manufacturing
• 11.8. Retail
• 11.9. Telecommunication & IT

12. Americas Synthetic Data Generation Market

• 12.1. Introduction
• 12.2. Argentina
• 12.3. Brazil
• 12.4. Canada
• 12.5. Mexico
• 12.6. United States

13. Asia-Pacific Synthetic Data Generation Market

• 13.1. Introduction
• 13.2. Australia
• 13.3. China
• 13.4. India
• 13.5. Indonesia
• 13.6. Japan
• 13.7. Malaysia
• 13.8. Philippines
• 13.9. Singapore
• 13.10. South Korea
• 13.11. Taiwan
• 13.12. Thailand
• 13.13. Vietnam

14. Europe, Middle East & Africa Synthetic Data Generation Market

• 14.1. Introduction
• 14.2. Denmark
• 14.3. Egypt
• 14.4. Finland
• 14.5. France
• 14.6. Germany
• 14.7. Israel
• 14.8. Italy
• 14.9. Netherlands
• 14.10. Nigeria
• 14.11. Norway
• 14.12. Poland
• 14.13. Qatar
• 14.14. Russia
• 14.15. Saudi Arabia
• 14.16. South Africa
• 14.17. Spain
• 14.18. Sweden
• 14.19. Switzerland
• 14.20. Turkey
• 14.21. United Arab Emirates
• 14.22. United Kingdom

15. Competitive Landscape

• 15.1. Market Share Analysis, 2023
• 15.2. FPNV Positioning Matrix, 2023
• 15.3. Competitive Scenario Analysis
  • 15.3.1. South Korea's PIPC launches synthetic data model promoting privacy-enhanced AI learning
  • 15.3.2. Rendered.ai and Carahsoft's strategic partnership for AI-driven synthetic data solutions
  • 15.3.3. Aindo secures EUR 6 million to lead synthetic data revolution with global AI expansion
  • 15.3.4. IBM advances watsonx AI and Data Platform with tech preview for watsonx. governance
  • 15.3.5. Tech Mahindra and Anyverse partner to accelerate AI adoption in the automotive industry
  • 15.3.6. Google Cloud partner Synthesized drives data transformations through Generative AI
• 15.4. Strategy Analysis & Recommendation
  • 15.4.1. Hazy Limited

Companies Mentioned

• 1. Amazon Web Services, Inc.
• 2. ANONOS INC.
• 3. BetterData Pte Ltd
• 4. Broadcom Corporation
• 5. Capgemini SE
• 6. ChipIn Inc.
• 7. Datawizz.ai
• 8. Folio3 Software Inc.
• 9. GenRocket, Inc.
• 10. Gretel Labs, Inc.
• 11. Hazy Limited
• 12. Informatica Inc.
• 13. International Business Machines Corporation
• 14. K2view Ltd.
• 15. Kroop AI Private Limited
• 16. Kymera-labs
• 17. MDClone Limited
• 18. Microsoft Corporation
• 19. MOSTLY AI
• 20. SAEC / Kinetic Vision, Inc.
• 21. Synthesis AI, Inc.
• 22. Synthesized Ltd.
• 23. Synthon International Holding B.V.
• 24. TonicAI, Inc.
• 25. YData Labs Inc.