首頁 > 市場調查報告書 > 通訊

巨量資料

市場調查報告書

商品編碼

1736917

全球資料收集和標籤市場規模（按類型、應用、區域範圍）預測至 2025 年

Global Data Collection and Labeling Market Size by Type (Text, Image/Video), By Application (Automotive, Healthcare), By Geographic Scope and Forecast

出版日期: 2025年05月08日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

價格

簡介目錄

資料收集和標籤市場規模及預測

2024 年資料收集和標籤市場規模價值 181.8 億美元，預計到 2032 年將達到 933.7 億美元，在 2026-2032 年預測期內的複合年成長率為 25.03%。

資料收集和標記涉及獲取原始資料並對其進行註釋，以用於機器學習和人工智慧應用。這項技術可確保資料集的結構化和準確性，從而使電腦能夠有效率地學習。圖像、文字和音訊是各行各業智慧系統開發中常用的資料類型。

在實踐中，收集和標記資料對於醫療保健、銀行和自動駕駛汽車等行業的模型訓練至關重要。提供高品質的學習輸入可以提高人工智慧應用程式的效能。工具和系統正在逐步實現這一過程的自動化，在提高數據品質的同時節省時間和精力。

隨著人工智慧和機器學習應用的日益普及，對資料收集和標記的需求也將日益成長。自動註釋和合成資料合成是簡化此流程的兩項創新。這項發展將使企業能夠更有效地利用數據，增強決策能力，並推動各領域的創新。

數據收集和標籤的全球市場動態

影響全球數據收集和標籤市場的關鍵市場動態是：

關鍵市場促進因素

對人工智慧和機器學習的依賴日益增加：隨著人工智慧和機器學習在眾多行業中的普及，對可靠數據收集和分類的需求也日益成長。到2025年，人工智慧產業規模預計將達到1,260億美元，凸顯了高品質資料集對於有效建模的重要性。

更重視資料隱私和合規性：GDPR 和 CCPA 等更高要求要求企業優先考慮確保隱私和合規性的資料收集方法。預計到 2023 年，全球資料隱私產業規模將成長至 67 億美元，凸顯了在標籤流程中採取負責任的資料處理實務的必要性。

高階資料註解工具的興起：高階資料註解工具的興起源自於技術進步，這些進步能夠提高效率並降低成本。全球數據註釋工具市場預計將顯著成長，因為它能夠促進更快、更準確的數據標記，這對於滿足日益成長的人工智慧應用需求至關重要。

主要問題

確保數據品質和準確性：保持高準確性是數據收集和標記過程中最艱鉅的挑戰之一。標記不良的數據可能會損害人工智慧模型的效能。確保大型資料集（尤其是照片和音訊等複雜資料類型）的質量，需要大量的人工監控和嚴格的通訊協定。

資料標註的可擴展性：AI 模型需要大量標註數據，這使得標註流程難以擴展。手動標註耗時耗力，在保持高效能的同時滿足日益成長的數據需求是一項挑戰，尤其對於需要特定領域知識的複雜資料集。

資料隱私問題：隨著《一般資料保護規範》(GDPR) 和《加州消費者隱私法案》(CCPA) 等資料隱私法規的不斷增多，在保護敏感資訊的同時收集和分類資料已成為一項重大挑戰。企業必須兼顧法律要求，並確保匿名化、知情同意和合規性，這增加了資料收集和標記流程的複雜性和成本。

主要趨勢

資料標註自動化應用日益普及：資料標註自動化正日益普及，從而節省了時間和人事費用成本。人工智慧系統如今能夠以更高的精度處理大規模標註任務。預計2020-2027年全球數據標註工具市場將以27.1%的複合年成長率成長，加速當前趨勢。

對高品質訓練資料的需求日益成長：隨著人工智慧系統日益複雜，對標記資料的需求也日益成長。準確的數據收集和標記對於開發可靠的機器學習模型至關重要。受此需求推動，預計到 2030 年，全球數據收集和標記市場將大幅成長。

合成資料的標記應用日益增多：為了解決資料稀缺和隱私問題，合成資料的使用日益增多，這使得企業即使沒有真實資料也能產生標記資料集。到2027年，合成資料的使用預計將對自動駕駛汽車和醫療保健等領域產生重大影響，從而增強模型訓練。

Data collecting and labeling entails acquiring raw data and annotating it for machine learning and AI applications. This technique guarantees that datasets are structured and accurate, allowing computers to learn efficiently. Images, text, and audio are common data types used in the development of intelligent systems in a variety of industries.

In practice, data collection and labeling are critical for training models in industries like as healthcare, banking, and autonomous cars. They help AI applications perform better by supplying high-quality learning inputs. Tools and systems are progressively automating this process, saving time and effort while enhancing data quality.

As AI and machine learning applications become more prevalent, the requirement for data collecting and labeling will increase. Automated annotation and synthetic data synthesis are two innovations that will streamline the process. This evolution will empower businesses to leverage data more efficiently, enhancing decision-making and driving innovation in various fields.

Global Data Collection And Labeling Market Dynamics

The key market dynamics that are shaping the global Data Collection And Labeling Market include:

Key Market Drivers:

Increasing Reliance on Artificial Intelligence and Machine Learning: As AI and machine learning become more prevalent in numerous industries, the necessity for reliable data gathering and categorization grows. By 2025, the AI business is estimated to be worth $126 billion, emphasizing the significance of high-quality datasets for effective modeling.

Increasing Emphasis on Data Privacy and Compliance: With stronger requirements such as GDPR and CCPA, enterprises must prioritize data collection methods that assure privacy and compliance. The global data privacy industry is expected to grow to USD 6.7 Billion by 2023, highlighting the need for responsible data handling methods in labeling processes.

Emergence Of Advanced Data Annotation Tools: The emergence of enhanced data annotation tools is being driven by technological improvements, which are improving efficiency and lowering costs. Global Data Annotation tools market is expected to grow significantly, facilitating faster and more accurate labeling of data, essential for meeting the increasing demands of AI applications.

Key Challenges:

Ensuring Data Quality and Accuracy: Maintaining high accuracy is one of the most difficult challenges in data gathering and labeling. Poorly labeled data can impair AI model performance. Ensuring quality across huge datasets, particularly for complex data types such as photos and audio, necessitates extensive human monitoring and rigorous protocols.

Scalability Of Data Labeling: As AI models require massive amounts of labeled data, scaling the labeling process becomes difficult. Manual labeling is time-consuming and resource-intensive, making it challenging for businesses to fulfil increasing data needs while remaining efficient, particularly for complex datasets requiring domain-specific knowledge.

Data Privacy Concerns: With more data privacy rules, such as GDPR and CCPA, collecting and categorizing data while protecting sensitive information is a significant difficulty. Organizations must navigate legal requirements and ensure anonymization, consent, and compliance, adding complexity and cost to the data collection and labeling processes.

Key Trends:

Rising Adoption of Automation in Data Labeling: Automation in data labeling is becoming more popular, saving time and personnel expenses. AI-powered systems now handle large-scale annotating tasks with greater accuracy. The global data annotation tools market is expected to develop at a CAGR of 27.1% between 2020 and 2027, accelerating the current trend.

Growing Demand for High-Quality Training Data: As AI systems get more complicated, there is a greater requirement for labeled data. Accurate data collection and labeling are critical for developing dependable machine learning models. The global Data Collection And Labeling Market is predicted to develop significantly by 2030 as a result of this demand.

Increasing the Use of Synthetic Data for Labeling: To address data shortages and privacy problems, the usage of synthetic data is increasing. It allows companies to generate labeled datasets without real-world data. By 2027, synthetic data usage is expected to significantly impact sectors like autonomous vehicles and healthcare, enhancing model training.

Global Data Collection And Labeling Market Regional Analysis

Here is a more detailed regional analysis of the global Data Collection And Labeling Market:

North America:

According to Verified Market Research, North America is expected to dominate the global Data Collection And Labeling Market.

The increasing growth of the AI and machine learning businesses in North America, particularly in the United States, is driving high demand for labeled data. The National Science Foundation reports that between 2011 and 2020, AI-related papers in North America increased by 198%.

The US Bureau of Labor Statistics predicts a 21% increase in AI-related employment by 2032. North American businesses are also aggressively investing in big data and analytics, which drives up demand for data collecting and labeling. The US big data market is projected at USD 200.5 Billion in 2020 and is anticipated to reach USD 292.1 Billion by 2025.

Asia Pacific:

According to Verified Market Research, Asia Pacific is fastest growing region in global Data Collection And Labeling Market.

Rapid digital transformation in Asia Pacific is driving up demand for data collecting and labeling services. Digital transformation spending in the region (excluding Japan) is expected to reach USD 1.2 Trillion by 2024, with a CAGR of 17.4%. This spike reflects the growing demand for labeled data to assist AI and machine learning.

The growing e-commerce sector and mobile internet usage are also driving data labeling need. Southeast Asia, for example, added 40 million internet users in 2020, bringing the total to 400 million. By 2025, the region's digital economy is estimated to be worth USD 360 Billion, necessitating considerable data labeling for improved user experience and customization.

Global Data Collection And Labeling Market: Segmentation Analysis

The Global Data Collection And Labeling Market is segmented based on Type, Application, and Geography.

Data Collection And Labeling Market, By Type

Text
Image/Video
Audio

Based on Type, the Global Data Collection And Labeling Market is separated into Text, Image/Video, and Audio. Image/Video leads the global Data Collection And Labeling Market due to its broad use in industries such as autonomous driving, healthcare diagnostics and facial recognition. The requirement for labeled visual data is critical for training AI and machine learning models, which is increasing its market share.

Data Collection And Labeling Market, By Application

Automotive
Healthcare
Banking, Financial Services and Insurance (BFSI)
Retail and E-commerce
IT and Telecom
Government

Based on Application, the Global Data Collection And Labeling Market is divided into Automotive, Healthcare, BFSI, Retail and E-commerce, IT and Telecom, Government. The automotive industry currently dominates the global Data Collection And Labeling Market, owing to the increasing demand for labeled data for autonomous driving systems, improved driver support systems and vehicle recognition technologies. The demand for accurate and comprehensive data in these applications necessitates major investment in data labeling systems.

Data Collection And Labeling Market, By Geography

North America
Europe
Asia Pacific
Rest of the World

Based on Geography, the Global Data Collection And Labeling Market divided into North America, Europe, Asia Pacific and Rest of the World. North America dominates the Data Collection And Labeling Market due to the high concentration of AI and IT businesses, which drives demand for labeled data. The Asia-Pacific area is the fastest growing, driven by rapid digital transformation, rising AI usage and emerging industries including as manufacturing and e-commerce that require tagged data.

Key Players

The Global Data Collection And Labeling Market study report will provide valuable insight with an emphasis on the global market. The major players in the market are Reality AI, Globalme Localization, Inc., Global Technology Solutions, Alegion, Labelbox, Inc., Dobility, Inc., Scale AI, Inc., Trilldata Technologies Pvt Ltd, Appen Limited, Playment, Inc.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share and market ranking analysis of the above-mentioned players globally.

Global Data Collection And Labeling Market Recent Developments
In November 2022, Scale AI bought Labelbox, a data labeling tool provider to enhance its data annotation capabilities and speed the development of its artificial intelligence platform.
In November 2022, Google introduced Cloud Annotations, a new data tagging platform. The platform employs machine learning to detect and classify things in photos and videos, saving time and effort over manual labeling. The software also allows users to collaborate on labeling activities, which makes large-scale labeling projects more manageable.

1 INTRODUCTION OF GLOBAL DATA COLLECTION AND LABELING MARKET

1.1 Overview of the Market
1.2 Scope of Report
1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources

4 GLOBAL DATA COLLECTION AND LABELING MARKET OUTLOOK

4.1 Overview
4.2 Market Dynamics
- 4.2.1 Drivers
- 4.2.2 Restraints
- 4.2.3 Opportunities

5 GLOBAL DATA COLLECTION AND LABELING MARKET, BY TYPE

5.1 Overview
5.2 Text
5.3 Image/Video
5.4 Audio

6 GLOBAL DATA COLLECTION AND LABELING MARKET, BY APPLICATION

6.1 Overview
6.2 Automotive
6.3 Healthcare
6.4 BFSI
6.5 Retail and E-commerce
6.6 IT and Telecom
6.7 Government

7 GLOBAL DATA COLLECTION AND LABELING MARKET, BY GEOGRAPHY

7.1 Overview
7.2 North America
- 7.2.1 U.S.
- 7.2.2 Canada
- 7.2.3 Mexico
7.3 Europe
- 7.3.1 Germany
- 7.3.2 U.K.
- 7.3.3 France
- 7.3.4 Rest of Europe
7.4 Asia Pacific
- 7.4.1 China
- 7.4.2 Japan
- 7.4.3 India
- 7.4.4 Rest of Asia Pacific
7.5 Rest of the World
- 7.5.1 Middle East & Africa
- 7.5.2 Latin America