Global Data Collection and Labelling Market

Data Collection and Labelling Comprehensive Study by Type (Computer Vision, Natural Language Processing, Audio Processing), Application (Image Recognition, Natural Language Processing (NLP), Healthcare, Finance), Data Type (Audio, Image/Video, Text), End Use (Automotive, Healthcare, IT & Telecommunication, BFSI, Others) Players and Region - Global Market Outlook to 2028

Data Collection and Labelling Market by XX Submarkets | Forecast Years 2023-2028  

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  • Summary
  • Market Segments
  • Table of Content
  • List of Table & Figures
  • Players Profiled
About Data Collection and Labelling
Data collection and Labelling is the process of identifying raw data and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. This labels are used to indicate whether a photo contains a bird or car, which words were uttered in an audio recording. Data Labelling is essential for a variety of use cases including computer vision, natural language processing, and speech recognition.

AttributesDetails
Study Period2018-2028
Base Year2022
UnitValue (USD Million)


The market of Data Collection and Labelling is expected to grow with rising demand for 3D imaging. Several companies are operating in the market to provide Data Collection and Labelling technology with number of unique features. The market is highly fragmented with the presence of several market players. They are gaining attention owing to their innovative and novel portfolio. Analyst at AMA Research estimates that United States Players will contribute the maximum growth to Global Data Collection and Labelling market throughout the forecasted period. Established and emerging Players should take a closer view at their existing organizations and reinvent traditional business and operating models to adapt to the future.

Reality AI (United States), Globalme Localization Inc. (Canada), Alegion (Austria), Labelbox, Inc. (United States), Dobility, Inc. (United States) and Scale AI, Inc. (United States) are some of the key players that are part of study coverage. Additionally, the Players which are also part of the research coverage are Trilldata Technologies Pvt Ltd (India), Appen Limited (Australia) and Playment Inc. (United States).

Segmentation Overview
AMA Research has segmented the market of Global Data Collection and Labelling market by Type (Computer Vision, Natural Language Processing and Audio Processing), Application (Image Recognition, Natural Language Processing (NLP), Healthcare and Finance) and Region.



On the basis of geography, the market of Data Collection and Labelling has been segmented into South America (Brazil, Argentina, Rest of South America), Asia Pacific (China, Japan, India, South Korea, Taiwan, Australia, Rest of Asia-Pacific), Europe (Germany, France, Italy, United Kingdom, Netherlands, Rest of Europe), MEA (Middle East, Africa), North America (United States, Canada, Mexico). If we see Market by Data Type, the sub-segment i.e. Audio will boost the Data Collection and Labelling market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth. If we see Market by End Use, the sub-segment i.e. Automotive will boost the Data Collection and Labelling market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth.

Influencing Trend:
Growing R&D spending on the development of self-driving vehicles, Crowdsourcing and distributed labor for data labeling. and Active learning techniques to reduce labeling requirements.

Market Growth Drivers:
Growing End Use Expenditure Has Created Demand and Increasing Adoptions for Cloud Based Service

Challenges:
Maintaining data quality and consistency across large datasets. and Scaling up labeling processes to meet the demand for diverse datasets.

Restraints:
Privacy concerns and ethical considerations related to data collection and labeling. and Difficulty in obtaining high-quality labeled data, especially in specialized domains.

Opportunities:
Market growth for data labeling services and tools. and Leveraging AI itself to automate and improve data labeling processes.

Market Leaders and their expansionary development strategies
In July 2021, Algorithmia Inc., .-based Machine Learning Operations (MLOps) software platform, was purchased by DataRobot, Inc. The platform, which was developed to meet the needs of IT operations specialists, enables businesses to handle the generation of complicated models in huge volumes in a safe and effective manner.
InIn December 2021, To speed up manufacturing data labeling, Sight Machine, a provider of a digital manufacturing platform meant to address fundamental issues with quality and productivity throughout the organization, announced a partnership with NVIDIA Corp. By linking its streaming data pipeline to the NVIDIA AI platform, which utilizes Microsoft Azure infrastructure to find data to assets on a global scale, Sight Machine hopes to get past the difficulty of categorizing data.
The FD&C Act applies to food, drugs, cosmetics, biologics, and medical devices. Section 201 defines the terms "label", and "labeling" as they ap ly to medical devices and draws a distinction between, these terms. Section 502( ? )(I) and (2) requires that device labeling bear adequate, directions for use, operating and servicing instructions, and either adequate, warnings a ainst dangerous uses to health, or information necessary for the and protection.

Key Target Audience
Data Collection and Labelling Providers, New Entrants/Investors, Strategic Business Planners, Governments and End Use Industry

About Approach
To evaluate and validate the market size various sources including primary and secondary analysis is utilized. AMA Research follows regulatory standards such as NAICS/SIC/ICB/TRCB, to have a better understanding of the market. The market study is conducted on basis of more than 200 companies dealing in the market regional as well as global areas with the purpose to understand the companies positioning regarding the market value, volume, and their market share for regional as well as global.

Further to bring relevance specific to any niche market we set and apply a number of criteria like Geographic Footprints, Regional Segments of Revenue, Operational Centres, etc. The next step is to finalize a team (In-House + Data Agencies) who then starts collecting C & D level executives and profiles, Industry experts, Opinion leaders, etc., and work towards appointment generation.

The primary research is performed by taking the interviews of executives of various companies dealing in the market as well as using the survey reports, research institute, and latest research reports. Meanwhile, the analyst team keeps preparing a set of questionnaires, and after getting the appointee list; the target audience is then tapped and segregated with various mediums and channels that are feasible for making connections that including email communication, telephonic, skype, LinkedIn Group & InMail, Community Forums, Community Forums, open Survey, SurveyMonkey, etc.

Report Objectives / Segmentation Covered

By Type
  • Computer Vision
  • Natural Language Processing
  • Audio Processing
By Application
  • Image Recognition
  • Natural Language Processing (NLP)
  • Healthcare
  • Finance
By Data Type
  • Audio
  • Image/Video
  • Text

By End Use
  • Automotive
  • Healthcare
  • IT & Telecommunication
  • BFSI
  • Others

By Regions
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Taiwan
    • Australia
    • Rest of Asia-Pacific
  • Europe
    • Germany
    • France
    • Italy
    • United Kingdom
    • Netherlands
    • Rest of Europe
  • MEA
    • Middle East
    • Africa
  • North America
    • United States
    • Canada
    • Mexico
  • 1. Market Overview
    • 1.1. Introduction
    • 1.2. Scope/Objective of the Study
      • 1.2.1. Research Objective
  • 2. Executive Summary
    • 2.1. Introduction
  • 3. Market Dynamics
    • 3.1. Introduction
    • 3.2. Market Drivers
      • 3.2.1. Growing End Use Expenditure Has Created Demand
      • 3.2.2. Increasing Adoptions for Cloud Based Service
    • 3.3. Market Challenges
      • 3.3.1. Maintaining data quality and consistency across large datasets.
      • 3.3.2. Scaling up labeling processes to meet the demand for diverse datasets.
    • 3.4. Market Trends
      • 3.4.1. Growing R&D spending on the development of self-driving vehicles
      • 3.4.2. Crowdsourcing and distributed labor for data labeling.
      • 3.4.3. Active learning techniques to reduce labeling requirements.
  • 4. Market Factor Analysis
    • 4.1. Porters Five Forces
    • 4.2. Supply/Value Chain
    • 4.3. PESTEL analysis
    • 4.4. Market Entropy
    • 4.5. Patent/Trademark Analysis
  • 5. Global Data Collection and Labelling, by Type, Application, Data Type, End Use and Region (value) (2017-2022)
    • 5.1. Introduction
    • 5.2. Global Data Collection and Labelling (Value)
      • 5.2.1. Global Data Collection and Labelling by: Type (Value)
        • 5.2.1.1. Computer Vision
        • 5.2.1.2. Natural Language Processing
        • 5.2.1.3. Audio Processing
      • 5.2.2. Global Data Collection and Labelling by: Application (Value)
        • 5.2.2.1. Image Recognition
        • 5.2.2.2. Natural Language Processing (NLP)
        • 5.2.2.3. Healthcare
        • 5.2.2.4. Finance
      • 5.2.3. Global Data Collection and Labelling by: Data Type (Value)
        • 5.2.3.1. Audio
        • 5.2.3.2. Image/Video
        • 5.2.3.3. Text
      • 5.2.4. Global Data Collection and Labelling by: End Use (Value)
        • 5.2.4.1. Automotive
        • 5.2.4.2. Healthcare
        • 5.2.4.3. IT & Telecommunication
        • 5.2.4.4. BFSI
        • 5.2.4.5. Others
      • 5.2.5. Global Data Collection and Labelling Region
        • 5.2.5.1. South America
          • 5.2.5.1.1. Brazil
          • 5.2.5.1.2. Argentina
          • 5.2.5.1.3. Rest of South America
        • 5.2.5.2. Asia Pacific
          • 5.2.5.2.1. China
          • 5.2.5.2.2. Japan
          • 5.2.5.2.3. India
          • 5.2.5.2.4. South Korea
          • 5.2.5.2.5. Taiwan
          • 5.2.5.2.6. Australia
          • 5.2.5.2.7. Rest of Asia-Pacific
        • 5.2.5.3. Europe
          • 5.2.5.3.1. Germany
          • 5.2.5.3.2. France
          • 5.2.5.3.3. Italy
          • 5.2.5.3.4. United Kingdom
          • 5.2.5.3.5. Netherlands
          • 5.2.5.3.6. Rest of Europe
        • 5.2.5.4. MEA
          • 5.2.5.4.1. Middle East
          • 5.2.5.4.2. Africa
        • 5.2.5.5. North America
          • 5.2.5.5.1. United States
          • 5.2.5.5.2. Canada
          • 5.2.5.5.3. Mexico
  • 6. Data Collection and Labelling: Manufacturers/Players Analysis
    • 6.1. Competitive Landscape
      • 6.1.1. Market Share Analysis
        • 6.1.1.1. Top 3
    • 6.2. Peer Group Analysis (2022)
    • 6.3. BCG Matrix
    • 6.4. Company Profile
      • 6.4.1. Reality AI (United States)
        • 6.4.1.1. Business Overview
        • 6.4.1.2. Products/Services Offerings
        • 6.4.1.3. Financial Analysis
        • 6.4.1.4. SWOT Analysis
      • 6.4.2. Globalme Localization Inc. (Canada)
        • 6.4.2.1. Business Overview
        • 6.4.2.2. Products/Services Offerings
        • 6.4.2.3. Financial Analysis
        • 6.4.2.4. SWOT Analysis
      • 6.4.3. Alegion (Austria)
        • 6.4.3.1. Business Overview
        • 6.4.3.2. Products/Services Offerings
        • 6.4.3.3. Financial Analysis
        • 6.4.3.4. SWOT Analysis
      • 6.4.4. Labelbox, Inc. (United States)
        • 6.4.4.1. Business Overview
        • 6.4.4.2. Products/Services Offerings
        • 6.4.4.3. Financial Analysis
        • 6.4.4.4. SWOT Analysis
      • 6.4.5. Dobility, Inc. (United States)
        • 6.4.5.1. Business Overview
        • 6.4.5.2. Products/Services Offerings
        • 6.4.5.3. Financial Analysis
        • 6.4.5.4. SWOT Analysis
      • 6.4.6. Scale AI, Inc. (United States)
        • 6.4.6.1. Business Overview
        • 6.4.6.2. Products/Services Offerings
        • 6.4.6.3. Financial Analysis
        • 6.4.6.4. SWOT Analysis
  • 7. Global Data Collection and Labelling Sale, by Type, Application, Data Type, End Use and Region (value) (2023-2028)
    • 7.1. Introduction
    • 7.2. Global Data Collection and Labelling (Value)
      • 7.2.1. Global Data Collection and Labelling by: Type (Value)
        • 7.2.1.1. Computer Vision
        • 7.2.1.2. Natural Language Processing
        • 7.2.1.3. Audio Processing
      • 7.2.2. Global Data Collection and Labelling by: Application (Value)
        • 7.2.2.1. Image Recognition
        • 7.2.2.2. Natural Language Processing (NLP)
        • 7.2.2.3. Healthcare
        • 7.2.2.4. Finance
      • 7.2.3. Global Data Collection and Labelling by: Data Type (Value)
        • 7.2.3.1. Audio
        • 7.2.3.2. Image/Video
        • 7.2.3.3. Text
      • 7.2.4. Global Data Collection and Labelling by: End Use (Value)
        • 7.2.4.1. Automotive
        • 7.2.4.2. Healthcare
        • 7.2.4.3. IT & Telecommunication
        • 7.2.4.4. BFSI
        • 7.2.4.5. Others
      • 7.2.5. Global Data Collection and Labelling Region
        • 7.2.5.1. South America
          • 7.2.5.1.1. Brazil
          • 7.2.5.1.2. Argentina
          • 7.2.5.1.3. Rest of South America
        • 7.2.5.2. Asia Pacific
          • 7.2.5.2.1. China
          • 7.2.5.2.2. Japan
          • 7.2.5.2.3. India
          • 7.2.5.2.4. South Korea
          • 7.2.5.2.5. Taiwan
          • 7.2.5.2.6. Australia
          • 7.2.5.2.7. Rest of Asia-Pacific
        • 7.2.5.3. Europe
          • 7.2.5.3.1. Germany
          • 7.2.5.3.2. France
          • 7.2.5.3.3. Italy
          • 7.2.5.3.4. United Kingdom
          • 7.2.5.3.5. Netherlands
          • 7.2.5.3.6. Rest of Europe
        • 7.2.5.4. MEA
          • 7.2.5.4.1. Middle East
          • 7.2.5.4.2. Africa
        • 7.2.5.5. North America
          • 7.2.5.5.1. United States
          • 7.2.5.5.2. Canada
          • 7.2.5.5.3. Mexico
  • 8. Appendix
    • 8.1. Acronyms
  • 9. Methodology and Data Source
    • 9.1. Methodology/Research Approach
      • 9.1.1. Research Programs/Design
      • 9.1.2. Market Size Estimation
      • 9.1.3. Market Breakdown and Data Triangulation
    • 9.2. Data Source
      • 9.2.1. Secondary Sources
      • 9.2.2. Primary Sources
    • 9.3. Disclaimer
List of Tables
  • Table 1. Data Collection and Labelling: by Type(USD Million)
  • Table 2. Data Collection and Labelling Computer Vision , by Region USD Million (2017-2022)
  • Table 3. Data Collection and Labelling Natural Language Processing , by Region USD Million (2017-2022)
  • Table 4. Data Collection and Labelling Audio Processing , by Region USD Million (2017-2022)
  • Table 5. Data Collection and Labelling: by Application(USD Million)
  • Table 6. Data Collection and Labelling Image Recognition , by Region USD Million (2017-2022)
  • Table 7. Data Collection and Labelling Natural Language Processing (NLP) , by Region USD Million (2017-2022)
  • Table 8. Data Collection and Labelling Healthcare , by Region USD Million (2017-2022)
  • Table 9. Data Collection and Labelling Finance , by Region USD Million (2017-2022)
  • Table 10. Data Collection and Labelling: by Data Type(USD Million)
  • Table 11. Data Collection and Labelling Audio , by Region USD Million (2017-2022)
  • Table 12. Data Collection and Labelling Image/Video , by Region USD Million (2017-2022)
  • Table 13. Data Collection and Labelling Text , by Region USD Million (2017-2022)
  • Table 14. Data Collection and Labelling: by End Use(USD Million)
  • Table 15. Data Collection and Labelling Automotive , by Region USD Million (2017-2022)
  • Table 16. Data Collection and Labelling Healthcare , by Region USD Million (2017-2022)
  • Table 17. Data Collection and Labelling IT & Telecommunication , by Region USD Million (2017-2022)
  • Table 18. Data Collection and Labelling BFSI , by Region USD Million (2017-2022)
  • Table 19. Data Collection and Labelling Others , by Region USD Million (2017-2022)
  • Table 20. South America Data Collection and Labelling, by Country USD Million (2017-2022)
  • Table 21. South America Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 22. South America Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 23. South America Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 24. South America Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 25. Brazil Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 26. Brazil Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 27. Brazil Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 28. Brazil Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 29. Argentina Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 30. Argentina Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 31. Argentina Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 32. Argentina Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 33. Rest of South America Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 34. Rest of South America Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 35. Rest of South America Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 36. Rest of South America Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 37. Asia Pacific Data Collection and Labelling, by Country USD Million (2017-2022)
  • Table 38. Asia Pacific Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 39. Asia Pacific Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 40. Asia Pacific Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 41. Asia Pacific Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 42. China Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 43. China Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 44. China Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 45. China Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 46. Japan Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 47. Japan Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 48. Japan Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 49. Japan Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 50. India Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 51. India Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 52. India Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 53. India Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 54. South Korea Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 55. South Korea Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 56. South Korea Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 57. South Korea Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 58. Taiwan Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 59. Taiwan Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 60. Taiwan Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 61. Taiwan Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 62. Australia Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 63. Australia Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 64. Australia Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 65. Australia Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 66. Rest of Asia-Pacific Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 67. Rest of Asia-Pacific Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 68. Rest of Asia-Pacific Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 69. Rest of Asia-Pacific Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 70. Europe Data Collection and Labelling, by Country USD Million (2017-2022)
  • Table 71. Europe Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 72. Europe Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 73. Europe Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 74. Europe Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 75. Germany Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 76. Germany Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 77. Germany Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 78. Germany Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 79. France Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 80. France Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 81. France Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 82. France Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 83. Italy Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 84. Italy Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 85. Italy Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 86. Italy Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 87. United Kingdom Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 88. United Kingdom Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 89. United Kingdom Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 90. United Kingdom Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 91. Netherlands Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 92. Netherlands Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 93. Netherlands Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 94. Netherlands Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 95. Rest of Europe Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 96. Rest of Europe Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 97. Rest of Europe Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 98. Rest of Europe Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 99. MEA Data Collection and Labelling, by Country USD Million (2017-2022)
  • Table 100. MEA Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 101. MEA Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 102. MEA Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 103. MEA Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 104. Middle East Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 105. Middle East Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 106. Middle East Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 107. Middle East Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 108. Africa Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 109. Africa Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 110. Africa Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 111. Africa Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 112. North America Data Collection and Labelling, by Country USD Million (2017-2022)
  • Table 113. North America Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 114. North America Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 115. North America Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 116. North America Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 117. United States Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 118. United States Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 119. United States Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 120. United States Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 121. Canada Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 122. Canada Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 123. Canada Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 124. Canada Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 125. Mexico Data Collection and Labelling, by Type USD Million (2017-2022)
  • Table 126. Mexico Data Collection and Labelling, by Application USD Million (2017-2022)
  • Table 127. Mexico Data Collection and Labelling, by Data Type USD Million (2017-2022)
  • Table 128. Mexico Data Collection and Labelling, by End Use USD Million (2017-2022)
  • Table 129. Company Basic Information, Sales Area and Its Competitors
  • Table 130. Company Basic Information, Sales Area and Its Competitors
  • Table 131. Company Basic Information, Sales Area and Its Competitors
  • Table 132. Company Basic Information, Sales Area and Its Competitors
  • Table 133. Company Basic Information, Sales Area and Its Competitors
  • Table 134. Company Basic Information, Sales Area and Its Competitors
  • Table 135. Data Collection and Labelling: by Type(USD Million)
  • Table 136. Data Collection and Labelling Computer Vision , by Region USD Million (2023-2028)
  • Table 137. Data Collection and Labelling Natural Language Processing , by Region USD Million (2023-2028)
  • Table 138. Data Collection and Labelling Audio Processing , by Region USD Million (2023-2028)
  • Table 139. Data Collection and Labelling: by Application(USD Million)
  • Table 140. Data Collection and Labelling Image Recognition , by Region USD Million (2023-2028)
  • Table 141. Data Collection and Labelling Natural Language Processing (NLP) , by Region USD Million (2023-2028)
  • Table 142. Data Collection and Labelling Healthcare , by Region USD Million (2023-2028)
  • Table 143. Data Collection and Labelling Finance , by Region USD Million (2023-2028)
  • Table 144. Data Collection and Labelling: by Data Type(USD Million)
  • Table 145. Data Collection and Labelling Audio , by Region USD Million (2023-2028)
  • Table 146. Data Collection and Labelling Image/Video , by Region USD Million (2023-2028)
  • Table 147. Data Collection and Labelling Text , by Region USD Million (2023-2028)
  • Table 148. Data Collection and Labelling: by End Use(USD Million)
  • Table 149. Data Collection and Labelling Automotive , by Region USD Million (2023-2028)
  • Table 150. Data Collection and Labelling Healthcare , by Region USD Million (2023-2028)
  • Table 151. Data Collection and Labelling IT & Telecommunication , by Region USD Million (2023-2028)
  • Table 152. Data Collection and Labelling BFSI , by Region USD Million (2023-2028)
  • Table 153. Data Collection and Labelling Others , by Region USD Million (2023-2028)
  • Table 154. South America Data Collection and Labelling, by Country USD Million (2023-2028)
  • Table 155. South America Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 156. South America Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 157. South America Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 158. South America Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 159. Brazil Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 160. Brazil Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 161. Brazil Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 162. Brazil Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 163. Argentina Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 164. Argentina Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 165. Argentina Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 166. Argentina Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 167. Rest of South America Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 168. Rest of South America Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 169. Rest of South America Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 170. Rest of South America Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 171. Asia Pacific Data Collection and Labelling, by Country USD Million (2023-2028)
  • Table 172. Asia Pacific Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 173. Asia Pacific Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 174. Asia Pacific Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 175. Asia Pacific Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 176. China Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 177. China Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 178. China Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 179. China Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 180. Japan Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 181. Japan Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 182. Japan Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 183. Japan Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 184. India Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 185. India Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 186. India Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 187. India Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 188. South Korea Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 189. South Korea Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 190. South Korea Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 191. South Korea Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 192. Taiwan Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 193. Taiwan Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 194. Taiwan Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 195. Taiwan Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 196. Australia Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 197. Australia Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 198. Australia Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 199. Australia Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 200. Rest of Asia-Pacific Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 201. Rest of Asia-Pacific Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 202. Rest of Asia-Pacific Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 203. Rest of Asia-Pacific Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 204. Europe Data Collection and Labelling, by Country USD Million (2023-2028)
  • Table 205. Europe Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 206. Europe Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 207. Europe Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 208. Europe Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 209. Germany Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 210. Germany Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 211. Germany Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 212. Germany Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 213. France Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 214. France Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 215. France Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 216. France Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 217. Italy Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 218. Italy Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 219. Italy Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 220. Italy Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 221. United Kingdom Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 222. United Kingdom Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 223. United Kingdom Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 224. United Kingdom Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 225. Netherlands Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 226. Netherlands Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 227. Netherlands Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 228. Netherlands Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 229. Rest of Europe Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 230. Rest of Europe Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 231. Rest of Europe Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 232. Rest of Europe Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 233. MEA Data Collection and Labelling, by Country USD Million (2023-2028)
  • Table 234. MEA Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 235. MEA Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 236. MEA Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 237. MEA Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 238. Middle East Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 239. Middle East Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 240. Middle East Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 241. Middle East Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 242. Africa Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 243. Africa Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 244. Africa Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 245. Africa Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 246. North America Data Collection and Labelling, by Country USD Million (2023-2028)
  • Table 247. North America Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 248. North America Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 249. North America Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 250. North America Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 251. United States Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 252. United States Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 253. United States Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 254. United States Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 255. Canada Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 256. Canada Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 257. Canada Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 258. Canada Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 259. Mexico Data Collection and Labelling, by Type USD Million (2023-2028)
  • Table 260. Mexico Data Collection and Labelling, by Application USD Million (2023-2028)
  • Table 261. Mexico Data Collection and Labelling, by Data Type USD Million (2023-2028)
  • Table 262. Mexico Data Collection and Labelling, by End Use USD Million (2023-2028)
  • Table 263. Research Programs/Design for This Report
  • Table 264. Key Data Information from Secondary Sources
  • Table 265. Key Data Information from Primary Sources
List of Figures
  • Figure 1. Porters Five Forces
  • Figure 2. Supply/Value Chain
  • Figure 3. PESTEL analysis
  • Figure 4. Global Data Collection and Labelling: by Type USD Million (2017-2022)
  • Figure 5. Global Data Collection and Labelling: by Application USD Million (2017-2022)
  • Figure 6. Global Data Collection and Labelling: by Data Type USD Million (2017-2022)
  • Figure 7. Global Data Collection and Labelling: by End Use USD Million (2017-2022)
  • Figure 8. South America Data Collection and Labelling Share (%), by Country
  • Figure 9. Asia Pacific Data Collection and Labelling Share (%), by Country
  • Figure 10. Europe Data Collection and Labelling Share (%), by Country
  • Figure 11. MEA Data Collection and Labelling Share (%), by Country
  • Figure 12. North America Data Collection and Labelling Share (%), by Country
  • Figure 13. Global Data Collection and Labelling share by Players 2022 (%)
  • Figure 14. Global Data Collection and Labelling share by Players (Top 3) 2022(%)
  • Figure 15. BCG Matrix for key Companies
  • Figure 16. Reality AI (United States) Revenue, Net Income and Gross profit
  • Figure 17. Reality AI (United States) Revenue: by Geography 2022
  • Figure 18. Globalme Localization Inc. (Canada) Revenue, Net Income and Gross profit
  • Figure 19. Globalme Localization Inc. (Canada) Revenue: by Geography 2022
  • Figure 20. Alegion (Austria) Revenue, Net Income and Gross profit
  • Figure 21. Alegion (Austria) Revenue: by Geography 2022
  • Figure 22. Labelbox, Inc. (United States) Revenue, Net Income and Gross profit
  • Figure 23. Labelbox, Inc. (United States) Revenue: by Geography 2022
  • Figure 24. Dobility, Inc. (United States) Revenue, Net Income and Gross profit
  • Figure 25. Dobility, Inc. (United States) Revenue: by Geography 2022
  • Figure 26. Scale AI, Inc. (United States) Revenue, Net Income and Gross profit
  • Figure 27. Scale AI, Inc. (United States) Revenue: by Geography 2022
  • Figure 28. Global Data Collection and Labelling: by Type USD Million (2023-2028)
  • Figure 29. Global Data Collection and Labelling: by Application USD Million (2023-2028)
  • Figure 30. Global Data Collection and Labelling: by Data Type USD Million (2023-2028)
  • Figure 31. Global Data Collection and Labelling: by End Use USD Million (2023-2028)
  • Figure 32. South America Data Collection and Labelling Share (%), by Country
  • Figure 33. Asia Pacific Data Collection and Labelling Share (%), by Country
  • Figure 34. Europe Data Collection and Labelling Share (%), by Country
  • Figure 35. MEA Data Collection and Labelling Share (%), by Country
  • Figure 36. North America Data Collection and Labelling Share (%), by Country
List of companies from research coverage that are profiled in the study
  • Reality AI (United States)
  • Globalme Localization Inc. (Canada)
  • Alegion (Austria)
  • Labelbox, Inc. (United States)
  • Dobility, Inc. (United States)
  • Scale AI, Inc. (United States)
Additional players considered in the study are as follows:
Trilldata Technologies Pvt Ltd (India) , Appen Limited (Australia) , Playment Inc. (United States)
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Key Highlights of Report


Nov 2023 203 Pages 69 Tables Base Year: 2022 Coverage: 15+ Companies; 18 Countries

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Global Data Collection and Labelling Market Quantitative research analysis
Global Data Collection and Labelling Market Qualitative Industry Analysis
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Frequently Asked Questions (FAQ):

The standard version of the report profiles players such as Reality AI (United States), Globalme Localization Inc. (Canada), Alegion (Austria), Labelbox, Inc. (United States), Dobility, Inc. (United States) and Scale AI, Inc. (United States) etc.
The Study can be customized subject to feasibility and data availability. Please connect with our sales representative for further information.
"Growing R&D spending on the development of self-driving vehicles " is seen as one of major influencing trends for Data Collection and Labelling Market during projected period 2022-2028.
The Data Collection and Labelling market study includes a random mix of players, including both market leaders and some top growing emerging players. Connect with our sales executive to get a complete company list in our research coverage.

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