세계의 데이터 어노테이션 및 라벨링 시장 : 장래 예측 - 어노테이션 유형별, 전개 모드별, 기술 정세별, 기술 활용법별, 최종 사용자별, 지역별 분석(-2032년)

According to Stratistics MRC, the Global Data Annotation and Labeling Market is accounted for $1.5 billion in 2025 and is expected to reach $7.5 billion by 2032 growing at a CAGR of 25.9% during the forecast period. Data Annotation and Labeling is the process of enriching raw data with meaningful tags, labels, or metadata to make it understandable and usable for machine learning and artificial intelligence systems. This involves identifying and categorizing elements within datasets, such as images, text, audio, or video, to train algorithms for tasks like object detection, sentiment analysis, speech recognition, and autonomous driving. Accurate annotation ensures AI models can learn patterns effectively, improving their decision-making and predictive capabilities. It is a critical step in the AI development pipeline, bridging the gap between unstructured data and actionable insights.

Market Dynamics:

Driver:

Growth of cloud computing and big data

Enterprises are generating vast volumes of unstructured data from images videos text and sensor feeds that require labeling for model training. Cloud-native platforms support scalable annotation pipelines real-time collaboration and integration with storage and compute environments. Demand for automated and semi-automated annotation tools is rising across autonomous systems healthcare retail and finance. Platforms enable distributed workforce management quality control and annotation lifecycle tracking. These dynamics are propelling platform deployment across data-intensive and AI-driven ecosystems.

Restraint:

Issues related to poor quality of training data

Inconsistent labeling ambiguous categories and human error degrade algorithm accuracy and generalizability. Enterprises face challenges in maintaining annotation standards across distributed teams and outsourced vendors. Lack of domain-specific expertise and contextual understanding further complicates annotation quality in specialized fields like medical imaging or legal text. Platforms must invest in validation tools consensus mechanisms and reviewer training to ensure reliability. These constraints continue to hinder adoption across high-stakes and precision-critical AI applications.

Opportunity:

Focus on data quality and consistency

Enterprises are prioritizing annotation accuracy explainability and auditability to meet regulatory and performance requirements. Platforms support consensus scoring inter-annotator agreement and automated error detection across large datasets. Integration with data versioning model feedback loops and annotation analytics enhances quality control and continuous improvement. Demand for high-integrity labeled data is rising across finance healthcare autonomous systems and NLP. These trends are fostering growth across quality-centric and compliance-aligned annotation infrastructure.

Threat:

Scalability issues in annotation processes

Manual annotation remains labor-intensive and difficult to scale across large multimodal datasets. Enterprises struggle to balance speed accuracy and cost when deploying annotation teams or outsourcing to third-party providers. Lack of automation and workflow optimization degrades productivity and increases operational overhead. Platforms must invest in active learning synthetic data and annotation reuse to improve scalability. These limitations continue to constrain platform performance across high-volume and real-time annotation use cases.

Covid-19 Impact:

The pandemic disrupted annotation workflows workforce availability and data collection across global markets. Lockdowns and remote work delayed project timelines and reduced access to secure annotation environments. However demand for AI surged across healthcare e-commerce and automation driving investment in cloud-based and remote annotation platforms. Enterprises adopted hybrid workforce models automated tools and quality assurance systems to maintain continuity. Public awareness of AI applications and data ethics increased across consumer and policy circles. These shifts are reinforcing long-term investment in resilient scalable and quality-driven annotation infrastructure.

The enterprises segment is expected to be the largest during the forecast period

The enterprises segment is expected to account for the largest market share during the forecast period due to their data volume model complexity and compliance requirements across AI initiatives. Large organizations deploy annotation platforms across autonomous vehicles medical diagnostics fraud detection and customer analytics. Platforms support multi-team collaboration workflow customization and integration with internal data lakes and ML pipelines. Demand for scalable secure and auditable annotation infrastructure is rising across regulated and mission-critical sectors. Enterprises align annotation strategies with model governance data privacy and operational efficiency goals. These capabilities are boosting segment dominance across enterprise-scale annotation deployments.

The video annotation segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the video annotation segment is predicted to witness the highest growth rate as computer vision applications expand across autonomous systems surveillance retail and healthcare. Platforms support object tracking activity recognition and temporal segmentation across high-resolution and multi-frame datasets. Integration with edge devices cloud storage and real-time analytics enhances annotation efficiency and model performance. Demand for scalable and context-aware video labeling is rising across robotics smart cities and behavioral analytics. Vendors offer automation tools frame interpolation and annotation templates to accelerate throughput. These dynamics are driving rapid growth across video-centric annotation platforms and services.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its enterprise investment AI maturity and infrastructure readiness across data annotation technologies. Enterprises deploy platforms across autonomous driving healthcare finance and retail to support model training and compliance. Investment in cloud computing workforce development and annotation automation supports scalability and quality. Presence of leading vendors research institutions and regulatory frameworks drives innovation and standardization. Firms align annotation strategies with data governance AI ethics and performance optimization. These factors are propelling North America's leadership in data annotation commercialization and enterprise adoption.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR as digital transformation AI adoption and data generation converge across regional economies. Countries like India China Japan and South Korea scale annotation platforms across e-commerce healthcare manufacturing and smart infrastructure. Government-backed programs support AI workforce development startup incubation and cloud infrastructure expansion. Local providers offer multilingual culturally adapted and cost-effective solutions tailored to regional data types and compliance needs. Demand for scalable and inclusive annotation infrastructure is rising across public and private sectors. These trends are accelerating regional growth across data annotation innovation and deployment.

Key players in the market

Some of the key players in Data Annotation and Labeling Market include Appen, Scale AI, Labelbox, CloudFactory, iMerit, Amazon Web Services (AWS), Google Cloud, Microsoft Azure, TELUS International, Alegion, TaskUs, Playment, Hive, SuperAnnotate and Shaip.

Key Developments:

In April 2025, Scale AI expanded its partnership with the U.S. Department of Defense, supporting AI model validation and data labeling for national security applications. The collaboration includes annotated satellite imagery, synthetic data generation, and human-in-the-loop feedback for autonomous systems. It reinforces Scale's role in high-stakes, mission-critical AI deployments.

In March 2025, Appen partnered with Google Cloud Vertex AI to deliver human-in-the-loop data labeling for generative AI models. The collaboration enables scalable annotation workflows for text, image, and audio datasets, supporting model fine-tuning and safety validation. It positions Appen as a key contributor to responsible GenAI development across enterprise platforms.

Annotation Types Covered:

• Image Annotation
• Text Annotation
• Video Annotation
• Audio Annotation

Deployment Modes Covered:

• Cloud-Based
• On-Premise

Technology Landscapes Covered:

• Human-in-the-Loop Systems
• Machine Learning-Based Annotation
• Automation & Quality Control Tools
• Other Technology Landscapes

Technology Utilizations Covered:

• Manual Annotation
• Semi-Supervised Annotation
• Fully Automated Annotation

End Users Covered:

• Enterprises
• Small & Medium Enterprises (SMEs)
• Government Agencies
• Academic & Research Institutions
• Data Labeling Service Providers
• 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 2024, 2025, 2026, 2028, and 2032
• 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 Technology 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 Data Annotation and Labeling Market, By Annotation Type

• 5.1 Introduction
• 5.2 Image Annotation
  • 5.2.1 Bounding Boxes
  • 5.2.2 Semantic Segmentation
  • 5.2.3 Keypoint & Landmark Annotation
• 5.3 Text Annotation
  • 5.3.1 Named Entity Recognition (NER)
  • 5.3.2 Sentiment & Intent Tagging
  • 5.3.3 Part-of-Speech Tagging
• 5.4 Video Annotation
  • 5.4.1 Object Tracking
  • 5.4.2 Frame-by-Frame Labeling
• 5.5 Audio Annotation
  • 5.5.1 Speech Recognition
  • 5.5.2 Speaker Identification
  • 5.5.3 Acoustic Event Tagging

6 Global Data Annotation and Labeling Market, By Deployment Mode

• 6.1 Introduction
• 6.2 Cloud-Based
• 6.3 On-Premise

7 Global Data Annotation and Labeling Market, By Technology Landscape

• 7.1 Introduction
• 7.2 Human-in-the-Loop Systems
• 7.3 Machine Learning-Based Annotation
• 7.4 Automation & Quality Control Tools
• 7.5 Other Technology Landscapes

8 Global Data Annotation and Labeling Market, By Technology Utilization

• 8.1 Introduction
• 8.2 Manual Annotation
• 8.3 Semi-Supervised Annotation
• 8.4 Fully Automated Annotation

9 Global Data Annotation and Labeling Market, By End User

• 9.1 Introduction
• 9.2 Enterprises
• 9.3 Small & Medium Enterprises (SMEs)
• 9.4 Government Agencies
• 9.5 Academic & Research Institutions
• 9.6 Data Labeling Service Providers
• 9.7 Other End Users

10 Global Data Annotation and Labeling Market, By Geography

• 10.1 Introduction
• 10.2 North America
  • 10.2.1 US
  • 10.2.2 Canada
  • 10.2.3 Mexico
• 10.3 Europe
  • 10.3.1 Germany
  • 10.3.2 UK
  • 10.3.3 Italy
  • 10.3.4 France
  • 10.3.5 Spain
  • 10.3.6 Rest of Europe
• 10.4 Asia Pacific
  • 10.4.1 Japan
  • 10.4.2 China
  • 10.4.3 India
  • 10.4.4 Australia
  • 10.4.5 New Zealand
  • 10.4.6 South Korea
  • 10.4.7 Rest of Asia Pacific
• 10.5 South America
  • 10.5.1 Argentina
  • 10.5.2 Brazil
  • 10.5.3 Chile
  • 10.5.4 Rest of South America
• 10.6 Middle East & Africa
  • 10.6.1 Saudi Arabia
  • 10.6.2 UAE
  • 10.6.3 Qatar
  • 10.6.4 South Africa
  • 10.6.5 Rest of Middle East & Africa

11 Key Developments

• 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 11.2 Acquisitions & Mergers
• 11.3 New Product Launch
• 11.4 Expansions
• 11.5 Other Key Strategies

12 Company Profiling

• 12.1 Appen
• 12.2 Scale AI
• 12.3 Labelbox
• 12.4 CloudFactory
• 12.5 iMerit
• 12.6 Amazon Web Services (AWS)
• 12.7 Google Cloud
• 12.8 Microsoft Azure
• 12.9 TELUS International
• 12.10 Alegion
• 12.11 TaskUs
• 12.12 Playment
• 12.13 Hive
• 12.14 SuperAnnotate
• 12.15 Shaip