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µ¥ÀÌÅÍ ¶óº§¸µ ½ÃÀå : µ¥ÀÌÅÍ À¯Çüº°, ¾÷°èº°, Áö¿ªº°Data Labeling Market, By Data Type, By Vertical, By Geography |
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±âÁØ ¿¬µµ | 2024³â | ½ÃÀå ±Ô¸ð(2025³â) | 48¾ï 7,000¸¸ ´Þ·¯ |
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¿¹Ãø ±â°£ CAGR(2025-2032³â) : | 29.10% | °¡Ä¡ ¿¹Ãø(2032³â) | 291¾ï 1,000¸¸ ´Þ·¯ |
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Global Data Labeling Market is estimated to be valued at US$ 4.87 Bn in 2025 and is expected to reach US$ 29.11 Bn by 2032, growing at a compound annual growth rate (CAGR) of 29.1% from 2025 to 2032.
Report Coverage | Report Details | ||
---|---|---|---|
Base Year: | 2024 | Market Size in 2025: | USD 4.87 Bn |
Historical Data for: | 2020 To 2024 | Forecast Period: | 2025 To 2032 |
Forecast Period 2025 to 2032 CAGR: | 29.10% | 2032 Value Projection: | USD 29.11 Bn |
The global data labeling market has witnessed significant growth in recent times. With the rise of machine learning and artificial intelligence technologies, there is an increasing need for large volumes of accurate labeled data to train algorithms. Data labeling involves manually annotating datasets with relevant tags, categories, and metadata to enable machines to understand patterns and classify information. It is a highly time-consuming and labor-intensive process but is essential for developing self-learning systems. The growing adoption of AI across various industry verticals from automotive and manufacturing to healthcare and retail has boosted the demand for data annotation services. Additionally, continuous advancements in computer vision, natural language processing, and other cognitive applications require frequent updates of training data sets, driving long term growth opportunities for players in this market.
The global data labeling market is primarily driven by the rising deployment of AI and machine learning technologies across multiple domains. Advanced algorithms need large volumes of high-quality training datasets to produce useful outcomes. However, creating labeled datasets manually is an expensive and resource-intensive undertaking. This has propelled organizations to outsource data labeling activities to specialist third-party providers. Furthermore, factors like the shortage of skilled annotation talent and the growing computational capabilities of AI have accelerated the outsourcing of data annotation projects. However, ensuring quality control and accuracy across huge datasets annotated by remote teams poses a challenge. Additionally, managing copyright and privacy issues involving sensitive personal information can also restrain the market growth. Nevertheless, the increasing focus on computer vision and self-supervised learning is expected to bring more opportunities for players in this market.
This report provides in-depth analysis of the global data labeling market, and provides market size (US$ Billion) and compound annual growth rate (CAGR%) for the forecast period (2025-2032), considering 2024 as the base year
It elucidates potential revenue opportunities across different segments and explains attractive investment proposition matrices for this market
This study also provides key insights about market drivers, restraints, opportunities, new product launches or approvals, market trends, regional outlook, and competitive strategies adopted by key players
It profiles key players in the global data labeling market based on the following parameters - company highlights, products portfolio, key highlights, financial performance, and strategies
Key companies covered as a part of this study include Reality AI, Globalme Localization Inc., Global Technology Solutions, Alegion, Labelbox Inc., Scale AI Inc., Trilldata Technologies Pvt Ltd, Appen Limited, Playment Inc., Dobility Inc., CloudFactory, Mighty AI (acquired by Uber), Samasource, Cogito Tech LLC, and iMerit
Insights from this report would allow marketers and the management authorities of the companies to make informed decisions regarding their future product launches, type up-gradation, market expansion, and marketing tactics
The global data labeling market report caters to various stakeholders in this industry including investors, suppliers, product manufacturers, distributors, new entrants, and financial analysts
Stakeholders would have ease in decision-making through various strategy matrices used in analyzing the global data labeling market