세계의 합성 데이터 생성 시장 : 데이터 유형, 모델링, 전개 모델, 기업 규모, 용도, 최종 용도별 - 예측(2025-2030년)

The Synthetic Data Generation Market was valued at USD 576.02 million in 2024 and is projected to grow to USD 764.84 million in 2025, with a CAGR of 34.43%, reaching USD 3,400.23 million by 2030.

Synthetic data generation has rapidly evolved from a niche experimental technology to a vital component of modern digital transformation strategies across industries. This technology creates realistic data sets using algorithmic processes, enabling organizations to overcome challenges such as data privacy, limited access to real-world data, and the high costs associated with data acquisition. In today's competitive market, data has become the backbone of decision-making and innovation. Synthetic data not only replicates the breadth and complexity of real data, but it also provides a controlled environment for testing and validating machine learning models. With increasing regulatory constraints on data usage and rising cybersecurity concerns, organizations are embracing synthetic data generation to simulate scenarios, optimize operations, and drive continuous improvement. This introduction lays the groundwork for understanding the current state of the market and the transformative impact synthetic data will have on industries ranging from healthcare to retail. By leveraging high-fidelity simulated data, companies can accelerate innovation, enhance research and development efforts, and maintain a competitive edge while confidently navigating the evolving digital landscape.

Transformative Shifts in the Synthetic Data Generation Landscape: Emerging Trends and Disruptive Changes

In recent years, the synthetic data generation landscape has witnessed dramatic shifts that have redefined industry benchmarks and operational practices. Advanced algorithms and increased computational power have fostered an environment where high-quality, realistic synthetic data is available for a wide range of applications. Market dynamics are being significantly altered by breakthroughs in generative adversarial networks (GANs) and other deep-learning methods, which have improved the accuracy and diversity of the simulated datasets. Traditional data acquisition methods are rapidly giving way to innovative solutions that can be scaled quickly and tailored to specific business needs. Furthermore, the evolving regulatory environment, particularly around data privacy and security, has intensified the focus on synthetic data as a safer alternative. These dynamic market factors are encouraging more organizations to explore the benefits of simulation over real-world data acquisition, resulting in more agile research and development processes, faster go-to-market timelines, and a renewed focus on data-driven decision making. The continuous evolution is fostering a competitive environment where early adopters gain substantial advantages over peers who rely solely on conventional data collection methods.

Key Segmentation Insights: A Detailed Analysis of Market Dynamics Across Various Dimensions

The market for synthetic data generation is segmented in multiple key dimensions that offer valuable insights into the current and future trends. When analyzed based on data type, the study encompasses image and video data, tabular data, and text data, providing a spectrum that highlights the versatility of synthetic data applications. The segmentation further extends into modeling methods, where both agent-based modeling and direct modeling are considered, each addressing specific simulation needs and operational challenges. In the arena of deployment models, a clear distinction is made between cloud and on-premise solutions, reflecting the varying preferences and operational frameworks of modern enterprises. Additionally, enterprise size segmentation distinguishes between large enterprises and small and medium enterprises (SMEs), unveiling unique demands and tailored offerings within the market. The application spectrum is robust, covering areas such as artificial intelligence and machine learning training and development, data analytics and visualization, enterprise data sharing, and test data management. Finally, the end-use segmentation spans a diverse range of industries including automotive and transportation, BFSI, government and defense, healthcare and life sciences, IT and ITeS, manufacturing, and retail and e-commerce. These multifaceted segmentation insights illustrate how organizations are leveraging synthetic data to address sector-specific challenges and capitalize on emerging opportunities.

Based on Data Type, market is studied across Image & Video Data, Tabular Data, and Text Data.

Based on Modelling, market is studied across Agent-based Modeling and Direct Modeling.

Based on Deployment Model, market is studied across Cloud and On-Premise.

Based on Enterprise Size, market is studied across Large Enterprises and Small and Medium Enterprises (SMEs).

Based on Application, market is studied across AI/ML Training and Development, Data analytics and visualization, Enterprise Data Sharing, and Test Data Management.

Based on End-use, market is studied across Automotive & Transportation, BFSI, Government & Defense, Healthcare & Life sciences, IT and ITeS, Manufacturing, and Retail & E-commerce.

Key Regional Insights: Performance, Opportunities, and Trends Across Global Markets

A regional analysis of the synthetic data generation market reveals divergent trends and emerging opportunities across major global territories. Insights drawn from the Americas indicate that innovation remains at the forefront, driven by a robust technology ecosystem and strong investment in cutting-edge research. In Europe, the Middle East, and Africa, the market is characterized by a steady adoption rate enhanced by regulatory support and a focus on data protection guidelines, which has spurred the integration of synthetic data in sectors like manufacturing and government services. Meanwhile, the Asia-Pacific region is witnessing exponential growth fueled by rapid digitalization, escalating investments in artificial intelligence, and a surge in the adoption of advanced data-driven methodologies. Each region contributes unique perspectives and operational advantages; from the research-centric approaches of the Americas to the regulatory innovation seen in EMEA, and the digital momentum emerging from Asia-Pacific, there exists a confluence of trends that are collectively shaping the future landscape of data utilization. This regional diversification underscores the importance of localized strategies to tap into the specific needs and competitive dynamics presented by different geographical areas.

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.

Key Companies Insights: Profiles of Leading Innovators Shaping the Future

Industry leaders who are actively shaping the synthetic data generation market include a broad range of companies that bring unique strengths to the table. Notable players driving innovation and setting benchmarks include Amazon Web Services, Inc., which leverages its expansive cloud infrastructure, and ANONOS INC., known for its focus on privacy-enhancing technologies. BetterData Pte Ltd and Broadcom Corporation have established themselves through innovative technical solutions and strategic market positioning. Capgemini SE and Datawizz.ai contribute significant value through their consultancy expertise and advanced data analytics capabilities, while Folio3 Software Inc. and GenRocket, Inc. continue to push the limits of simulation software. Companies like Gretel Labs, Inc. and Hazy Limited have garnered attention for their advancements in generating high-fidelity synthetic data. Informatica Inc. and International Business Machines Corporation (IBM) provide robust, scalable solutions that have become industry standards. Other influential companies include K2view Ltd., Kroop AI Private Limited, and Kymera-labs, while MDClone Limited, Microsoft Corporation, MOSTLY AI, and NVIDIA Corporation further solidify their positions with innovative integrations. Final leaders such as SAEC / Kinetic Vision, Inc., Synthesis AI, Inc., Synthesized Ltd., Synthon International Holding B.V., TonicAI, Inc., and YData Labs Inc. enhance the market dynamics through their proactive research, emphasizing a competitive landscape where collaboration and continuous evolution remain key.

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, 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, NVIDIA Corporation, SAEC / Kinetic Vision, Inc., Synthesis AI, Inc., Synthesized Ltd., Synthon International Holding B.V., TonicAI, Inc., and YData Labs Inc.. Actionable Recommendations for Industry Leaders: Strategic Paths Forward for Competitive Advantage

For industry leaders seeking to harness the full potential of synthetic data generation, a set of strategic recommendations can guide decision-making and pave the way for long-term growth. Organizations should begin by investing in advanced analytics infrastructure that not only supports synthetic data creation but also seamlessly integrates with existing data workflows. It is imperative to focus on pilot projects that test the scalability and accuracy of synthetic datasets in real-world scenarios. Leaders should actively form cross-functional teams that include data scientists, software engineers, and domain experts to collaboratively explore how synthetic data solutions can be tailored to meet specific operational challenges. Monitoring the regulatory landscape closely is essential in order to adapt strategies as data privacy and protection standards evolve. Furthermore, fostering partnerships with technology innovators can substantially shorten the time-to-market for new applications and bolster overall competitive positioning. Embracing a culture of continuous learning and agile adaptation will enable companies to anticipate market shifts and leverage synthetic data innovations effectively. This proactive stance will not only improve operational efficiency but also create new revenue streams, ensuring sustainable growth and a decisive competitive edge in an ever-evolving marketplace.

Comprehensive Conclusion: Synthesis of Findings and Strategic Implications for the Future

The comprehensive analysis presented here underscores the transformative potential of synthetic data generation across multiple facets of modern business operations. The evolution from rudimentary data simulation to sophisticated, high-fidelity models has reshaped traditional approaches to data acquisition, methodology, and deployment. Detailed segmentation insights reveal that the market is not monolithic but rather a complex matrix of different data types, modeling strategies, deployment modes, enterprise sizes, application areas, and end-use sectors. Additionally, a geographic breakdown highlights how regional variations contribute to overall market dynamism, reflecting both mature and emerging trends. The in-depth profiles of leading companies within the market illustrate the robust interplay between technological innovation and strategic implementation, fostering an environment ripe for competitive differentiation. As organizations navigate this rapidly evolving landscape, the key lies in embracing cutting-edge technology, maintaining agility in response to regulatory shifts, and fostering collaborative innovation. Ultimately, the strategic integration of synthetic data technologies promises not just cost efficiency and enhanced security, but also the ability to unlock new avenues for growth, driving the future of digital transformation in a host of industries.

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 digitization across industries fuels the demand for advanced synthetic data generation
    • 5.1.1.2. Government regulations and compliance drive the adoption of synthetic data in several sectors
    • 5.1.1.3. Advancements in big data analytics fostering the need for large-scale synthetic data generation
  • 5.1.2. Restraints
    • 5.1.2.1. Data privacy concerns complicating synthetic data deployment across industries
  • 5.1.3. Opportunities
    • 5.1.3.1. Emerging AI & machine learning applications foster the demand for synthetic data solutions
    • 5.1.3.2. Adopting synthetic data for autonomous vehicle testing and safety protocol enhancement
  • 5.1.4. Challenges
    • 5.1.4.1. Technical difficulties in maintaining variability and realism in synthetic datasets
• 5.2. Market Segmentation Analysis
  • 5.2.1. Data Type: Growing demand for image & video data with the burgeoning growth of computer vision applications
  • 5.2.2. Deployment Model: Rising adoption of cloud deployment owing to scalability & cost-efficiency
• 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 Modelling

• 7.1. Introduction
• 7.2. Agent-based Modeling
• 7.3. Direct Modeling

8. Synthetic Data Generation Market, by Deployment Model

• 8.1. Introduction
• 8.2. Cloud
• 8.3. On-Premise

9. Synthetic Data Generation Market, by Enterprise Size

• 9.1. Introduction
• 9.2. Large Enterprises
• 9.3. Small and Medium Enterprises (SMEs)

10. Synthetic Data Generation Market, by Application

• 10.1. Introduction
• 10.2. AI/ML Training and Development
• 10.3. Data analytics and visualization
• 10.4. Enterprise Data Sharing
• 10.5. Test Data Management

11. Synthetic Data Generation Market, by End-use

• 11.1. Introduction
• 11.2. Automotive & Transportation
• 11.3. BFSI
• 11.4. Government & Defense
• 11.5. Healthcare & Life sciences
• 11.6. IT and ITeS
• 11.7. Manufacturing
• 11.8. Retail & E-commerce

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, 2024
• 15.2. FPNV Positioning Matrix, 2024
• 15.3. Competitive Scenario Analysis
  • 15.3.1. SAS acquisition of Hazy enhances AI capabilities with synthetic data, boosts privacy and accelerates development
  • 15.3.2. South Korea's PIPC launches synthetic data model promoting privacy-enhanced AI learning
  • 15.3.3. Rendered.ai and Carahsoft's strategic partnership for AI-driven synthetic data solutions
• 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. Datawizz.ai
• 7. Folio3 Software Inc.
• 8. GenRocket, Inc.
• 9. Gretel Labs, Inc.
• 10. Hazy Limited
• 11. Informatica Inc.
• 12. International Business Machines Corporation
• 13. K2view Ltd.
• 14. Kroop AI Private Limited
• 15. Kymera-labs
• 16. MDClone Limited
• 17. Microsoft Corporation
• 18. MOSTLY AI
• 19. NVIDIA Corporation
• 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.