Global Data Engineering, Preparation, and Labeling for AI Market

Data Engineering, Preparation, and Labeling for AI Comprehensive Study by Type (Data Engineering, Data Preparation, Data Labeling), Application (Data Labeling, Data Preparation, AI Applications in Engineering), End User Industry (Banking, Financial Services, and Insurance, Healthcare and Pharma, Retail, Technology, Media and Entertainment, Automotive, Transportation, Others), Project Type (Internal Development, Third-Party Solution), Organisation Size (SMEs, Large Enterprise) Players and Region - Global Market Outlook to 2028

Data Engineering, Preparation, and Labeling for AI Market by XX Submarkets | Forecast Years 2023-2028  

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  • Summary
  • Market Segments
  • Table of Content
  • List of Table & Figures
  • Players Profiled
Industry Background:
A company has data generally referred to as raw data or information buried in the text, figures, tables that the organization acquires in various business operations. This data is stored and at times it is unused to derive insights and for decision making in the business. Organizations nowadays are releasing that there are various risks associated with losing a competitive edge in the business and regulatory issues with not analyzing and processing it. Preparing data is more difficult and is time-consuming and expensive for an organization. In recent times, the amount of time spent in a typical machine learning AI project is on identifying, aggregating, cleaning, shaping, and labeling data to be used in machine learning models. In order to evaluate the requirements for that, data preparation solutions aim to clean, augment, and otherwise enhance data for machine learning purposes, data engineering solutions aim to give organizations a way to move and handle large volumes of data, and data labeling solutions that aim to augment data with the required annotations that are necessarily used in machine learning training models.This growth is primarily driven by Proliferation in Data Generation .

AttributesDetails
Study Period2018-2028
Base Year2022
Forecast Period2023-2028
Volume UnitN
Value UnitUSD (Million)
Customization ScopeAvail customization with purchase of this report. Add or modify country, region & or narrow down segments in the final scope subject to feasibility


The Data Storage and Management sector in the region has been increasing at a sustainable rate and further growth is expected to be witnessed over the forecast period, owing to the robust investments and expansion in production facilities in the region. Major Players, such as CloudFactory (United Kingdom), Figure Eight (United States), iMerit (India), Melissa Data (United States), Paxata (United States) and Trifacta (United States), etc have either set up their manufacturing facilities or are planning to start new provision in the dominated region in the upcoming years.

Key Developments in the Market:
In September 2022International law firm Osborne Clarke has advised CloudFactory, a global leader in human-in-the-loop artificial intelligence (AI), on its acquisition of Hasty, a data-centric machine learning (ML) platform that allows companies to build and deploy vision AI applications faster and more reliably.
In January 2023 N.C CloudFactory, a global leader in human-in-the-loop AI, has launched Accelerated Annotation, a Vision AI product that combines CloudFactory’s best-in-class workforce with industry-leading AI-assisted labeling technology that generates high-quality labeled data 5x faster than manual labeling.Data preparation and engineering tasks represent over 80% of the time consumed in most AI and Machine Learning projects. AI projects relating to object/image recognition, autonomous vehicles, and text and image annotation are the most common workloads for data labeling efforts. For every 1x dollar spent on Third-Party Data Labeling solutions, 2x dollars are spent on internal data efforts to support or enhance those labeling efforts. Within the next two years, all competitive data preparation tools will have machine learning augmented intelligence as a core part of the offering.

Influencing Trend:
Rising Adoption of Data Engineering, Preparation, and Labelling For AI in Large Enterprises

Market Growth Drivers:
Proliferation in Data Generation, Enterprise Need for Ensuring Market Competitiveness and Growing Adoption of Big Data and Other Related Technologies

Challenges:
Ownership and Privacy of the Collected Data.

Restraints:
Address data biases and ensure fairness in models.

Opportunities:
Growing Demand for Intelligent Business Processes, Rising Awareness Accelerating the Development of Better Analytics Tools and Increasing Adoption in Modern Applications

AMA Research follows a focused and realistic research framework that provides the ability to study the crucial market dynamics in several regions across the world. Moreover, an in-depth assessment is mainly conducted by our analysts on geographical regions to provide clients and businesses the opportunity to dominate in niche markets and expand in emerging markets across the globe. This market research study also showcases the spontaneously changing Players landscape impacting the market's growth. Furthermore, our market researchers extensively analyze the products and services offered by multiple players competing to increase their market share and presence.

Data Sources of Data Engineering, Preparation, and Labeling for AI Market Study

Primary Collection: InMail, LinkedIn Groups, Survey Monkey, Google, and Other professional Forums are some of the mediums utilized to gather primary data through key industry participants and appointees, subject-matter experts, C-level executives of Data Engineering, Preparation, and Labeling for AI Industry, among others including independent industry consultants, experts, to obtain and verify critical qualitative commentary and opinion and quantitative statistics, to assess future market prospects.

The primary interviews and data collected as per the below protocols:
• By Designation: C-Level, D-Level, Others
• By Company Type: Tier 1, Tier 2, Tier 3

Secondary Data Sources such as Annual reports, Press releases, Analyst meetings, Conference calls, Investor presentations, Management statements, and SEC filings of Data Engineering, Preparation, and Labeling for AI players along with Regulatory Sites, Association, World bank, etc were used as sources secondary set of data.

Customization in the Report
AMA Research features not only specific market forecasts but also includes significant value-added commentary on:
- Market Trends
- Technological Trends and Innovations
- Market Maturity Indicators
- Growth Drivers and Constraints
- New Entrants into the Market & Entry/Exit Barriers
- To Seize Powerful Market Opportunities
- Identify Key Business Segments, Market Proposition & Gap Analysis

Against this Challenging Backdrop, Data Engineering, Preparation, and Labeling for AI Study Sheds Light on
— The Data Engineering, Preparation, and Labeling for AI Market status quo and key characteristics. To end this, Analysts at AMA organize and took surveys of the Data Engineering, Preparation, and Labeling for AI industry Players. The resultant snapshot serves as a basis for understanding why and how the industry can be expected to change.
— Where Data Engineering, Preparation, and Labeling for AI industry is heading and what are the top priorities. Insights are drawn from financial analysis, surveys, and interviews with key executives and industry experts.
— How every company in this diverse set of Players can best navigate the emerging competition landscape and follow a strategy that helps them position to hold the value they currently claim or capture the new addressable opportunity.

Report Objectives / Segmentation Covered

By Type
  • Data Engineering
  • Data Preparation
  • Data Labeling
By Application
  • Data Labeling
  • Data Preparation
  • AI Applications in Engineering
By End User Industry
  • Banking, Financial Services, and Insurance
  • Healthcare and Pharma
  • Retail
  • Technology
  • Media and Entertainment
  • Automotive
  • Transportation
  • Others

By Project Type
  • Internal Development
  • Third-Party Solution

By Organisation Size
  • SMEs
  • Large Enterprise

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. Proliferation in Data Generation
      • 3.2.2. Enterprise Need for Ensuring Market Competitiveness
      • 3.2.3. Growing Adoption of Big Data and Other Related Technologies
    • 3.3. Market Challenges
      • 3.3.1. Ownership and Privacy of the Collected Data.
    • 3.4. Market Trends
      • 3.4.1. Rising Adoption of Data Engineering, Preparation, and Labelling For AI in Large Enterprises
  • 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 Engineering, Preparation, and Labeling for AI, by Type, Application, End User Industry, Project Type, Organisation Size and Region (value and price ) (2017-2022)
    • 5.1. Introduction
    • 5.2. Global Data Engineering, Preparation, and Labeling for AI (Value)
      • 5.2.1. Global Data Engineering, Preparation, and Labeling for AI by: Type (Value)
        • 5.2.1.1. Data Engineering
        • 5.2.1.2. Data Preparation
        • 5.2.1.3. Data Labeling
      • 5.2.2. Global Data Engineering, Preparation, and Labeling for AI by: Application (Value)
        • 5.2.2.1. Data Labeling
        • 5.2.2.2. Data Preparation
        • 5.2.2.3. AI Applications in Engineering
      • 5.2.3. Global Data Engineering, Preparation, and Labeling for AI by: End User Industry (Value)
        • 5.2.3.1. Banking, Financial Services, and Insurance
        • 5.2.3.2. Healthcare and Pharma
        • 5.2.3.3. Retail
        • 5.2.3.4. Technology
        • 5.2.3.5. Media and Entertainment
        • 5.2.3.6. Automotive
        • 5.2.3.7. Transportation
        • 5.2.3.8. Others
      • 5.2.4. Global Data Engineering, Preparation, and Labeling for AI by: Project Type (Value)
        • 5.2.4.1. Internal Development
        • 5.2.4.2. Third-Party Solution
      • 5.2.5. Global Data Engineering, Preparation, and Labeling for AI by: Organisation Size (Value)
        • 5.2.5.1. SMEs
        • 5.2.5.2. Large Enterprise
      • 5.2.6. Global Data Engineering, Preparation, and Labeling for AI Region
        • 5.2.6.1. South America
          • 5.2.6.1.1. Brazil
          • 5.2.6.1.2. Argentina
          • 5.2.6.1.3. Rest of South America
        • 5.2.6.2. Asia Pacific
          • 5.2.6.2.1. China
          • 5.2.6.2.2. Japan
          • 5.2.6.2.3. India
          • 5.2.6.2.4. South Korea
          • 5.2.6.2.5. Taiwan
          • 5.2.6.2.6. Australia
          • 5.2.6.2.7. Rest of Asia-Pacific
        • 5.2.6.3. Europe
          • 5.2.6.3.1. Germany
          • 5.2.6.3.2. France
          • 5.2.6.3.3. Italy
          • 5.2.6.3.4. United Kingdom
          • 5.2.6.3.5. Netherlands
          • 5.2.6.3.6. Rest of Europe
        • 5.2.6.4. MEA
          • 5.2.6.4.1. Middle East
          • 5.2.6.4.2. Africa
        • 5.2.6.5. North America
          • 5.2.6.5.1. United States
          • 5.2.6.5.2. Canada
          • 5.2.6.5.3. Mexico
    • 5.3. Global Data Engineering, Preparation, and Labeling for AI (Price)
      • 5.3.1. Global Data Engineering, Preparation, and Labeling for AI by: Type (Price)
  • 6. Data Engineering, Preparation, and Labeling for AI: 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. CloudFactory (United Kingdom)
        • 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. Figure Eight (United States)
        • 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. IMerit (India)
        • 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. Melissa Data (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. Paxata (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. Trifacta (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 Engineering, Preparation, and Labeling for AI Sale, by Type, Application, End User Industry, Project Type, Organisation Size and Region (value and price ) (2023-2028)
    • 7.1. Introduction
    • 7.2. Global Data Engineering, Preparation, and Labeling for AI (Value)
      • 7.2.1. Global Data Engineering, Preparation, and Labeling for AI by: Type (Value)
        • 7.2.1.1. Data Engineering
        • 7.2.1.2. Data Preparation
        • 7.2.1.3. Data Labeling
      • 7.2.2. Global Data Engineering, Preparation, and Labeling for AI by: Application (Value)
        • 7.2.2.1. Data Labeling
        • 7.2.2.2. Data Preparation
        • 7.2.2.3. AI Applications in Engineering
      • 7.2.3. Global Data Engineering, Preparation, and Labeling for AI by: End User Industry (Value)
        • 7.2.3.1. Banking, Financial Services, and Insurance
        • 7.2.3.2. Healthcare and Pharma
        • 7.2.3.3. Retail
        • 7.2.3.4. Technology
        • 7.2.3.5. Media and Entertainment
        • 7.2.3.6. Automotive
        • 7.2.3.7. Transportation
        • 7.2.3.8. Others
      • 7.2.4. Global Data Engineering, Preparation, and Labeling for AI by: Project Type (Value)
        • 7.2.4.1. Internal Development
        • 7.2.4.2. Third-Party Solution
      • 7.2.5. Global Data Engineering, Preparation, and Labeling for AI by: Organisation Size (Value)
        • 7.2.5.1. SMEs
        • 7.2.5.2. Large Enterprise
      • 7.2.6. Global Data Engineering, Preparation, and Labeling for AI Region
        • 7.2.6.1. South America
          • 7.2.6.1.1. Brazil
          • 7.2.6.1.2. Argentina
          • 7.2.6.1.3. Rest of South America
        • 7.2.6.2. Asia Pacific
          • 7.2.6.2.1. China
          • 7.2.6.2.2. Japan
          • 7.2.6.2.3. India
          • 7.2.6.2.4. South Korea
          • 7.2.6.2.5. Taiwan
          • 7.2.6.2.6. Australia
          • 7.2.6.2.7. Rest of Asia-Pacific
        • 7.2.6.3. Europe
          • 7.2.6.3.1. Germany
          • 7.2.6.3.2. France
          • 7.2.6.3.3. Italy
          • 7.2.6.3.4. United Kingdom
          • 7.2.6.3.5. Netherlands
          • 7.2.6.3.6. Rest of Europe
        • 7.2.6.4. MEA
          • 7.2.6.4.1. Middle East
          • 7.2.6.4.2. Africa
        • 7.2.6.5. North America
          • 7.2.6.5.1. United States
          • 7.2.6.5.2. Canada
          • 7.2.6.5.3. Mexico
    • 7.3. Global Data Engineering, Preparation, and Labeling for AI (Price)
      • 7.3.1. Global Data Engineering, Preparation, and Labeling for AI by: Type (Price)
  • 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 Engineering, Preparation, and Labeling for AI: by Type(USD Million)
  • Table 2. Data Engineering, Preparation, and Labeling for AI Data Engineering , by Region USD Million (2017-2022)
  • Table 3. Data Engineering, Preparation, and Labeling for AI Data Preparation , by Region USD Million (2017-2022)
  • Table 4. Data Engineering, Preparation, and Labeling for AI Data Labeling , by Region USD Million (2017-2022)
  • Table 5. Data Engineering, Preparation, and Labeling for AI: by Application(USD Million)
  • Table 6. Data Engineering, Preparation, and Labeling for AI Data Labeling , by Region USD Million (2017-2022)
  • Table 7. Data Engineering, Preparation, and Labeling for AI Data Preparation , by Region USD Million (2017-2022)
  • Table 8. Data Engineering, Preparation, and Labeling for AI AI Applications in Engineering , by Region USD Million (2017-2022)
  • Table 9. Data Engineering, Preparation, and Labeling for AI: by End User Industry(USD Million)
  • Table 10. Data Engineering, Preparation, and Labeling for AI Banking, Financial Services, and Insurance , by Region USD Million (2017-2022)
  • Table 11. Data Engineering, Preparation, and Labeling for AI Healthcare and Pharma , by Region USD Million (2017-2022)
  • Table 12. Data Engineering, Preparation, and Labeling for AI Retail , by Region USD Million (2017-2022)
  • Table 13. Data Engineering, Preparation, and Labeling for AI Technology , by Region USD Million (2017-2022)
  • Table 14. Data Engineering, Preparation, and Labeling for AI Media and Entertainment , by Region USD Million (2017-2022)
  • Table 15. Data Engineering, Preparation, and Labeling for AI Automotive , by Region USD Million (2017-2022)
  • Table 16. Data Engineering, Preparation, and Labeling for AI Transportation , by Region USD Million (2017-2022)
  • Table 17. Data Engineering, Preparation, and Labeling for AI Others , by Region USD Million (2017-2022)
  • Table 18. Data Engineering, Preparation, and Labeling for AI: by Project Type(USD Million)
  • Table 19. Data Engineering, Preparation, and Labeling for AI Internal Development , by Region USD Million (2017-2022)
  • Table 20. Data Engineering, Preparation, and Labeling for AI Third-Party Solution , by Region USD Million (2017-2022)
  • Table 21. Data Engineering, Preparation, and Labeling for AI: by Organisation Size(USD Million)
  • Table 22. Data Engineering, Preparation, and Labeling for AI SMEs , by Region USD Million (2017-2022)
  • Table 23. Data Engineering, Preparation, and Labeling for AI Large Enterprise , by Region USD Million (2017-2022)
  • Table 24. South America Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2017-2022)
  • Table 25. South America Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 26. South America Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 27. South America Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 28. South America Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 29. South America Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 30. Brazil Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 31. Brazil Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 32. Brazil Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 33. Brazil Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 34. Brazil Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 35. Argentina Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 36. Argentina Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 37. Argentina Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 38. Argentina Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 39. Argentina Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 40. Rest of South America Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 41. Rest of South America Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 42. Rest of South America Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 43. Rest of South America Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 44. Rest of South America Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 45. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2017-2022)
  • Table 46. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 47. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 48. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 49. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 50. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 51. China Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 52. China Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 53. China Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 54. China Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 55. China Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 56. Japan Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 57. Japan Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 58. Japan Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 59. Japan Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 60. Japan Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 61. India Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 62. India Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 63. India Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 64. India Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 65. India Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 66. South Korea Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 67. South Korea Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 68. South Korea Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 69. South Korea Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 70. South Korea Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 71. Taiwan Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 72. Taiwan Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 73. Taiwan Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 74. Taiwan Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 75. Taiwan Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 76. Australia Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 77. Australia Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 78. Australia Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 79. Australia Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 80. Australia Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 81. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 82. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 83. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 84. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 85. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 86. Europe Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2017-2022)
  • Table 87. Europe Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 88. Europe Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 89. Europe Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 90. Europe Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 91. Europe Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 92. Germany Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 93. Germany Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 94. Germany Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 95. Germany Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 96. Germany Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 97. France Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 98. France Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 99. France Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 100. France Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 101. France Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 102. Italy Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 103. Italy Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 104. Italy Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 105. Italy Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 106. Italy Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 107. United Kingdom Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 108. United Kingdom Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 109. United Kingdom Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 110. United Kingdom Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 111. United Kingdom Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 112. Netherlands Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 113. Netherlands Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 114. Netherlands Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 115. Netherlands Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 116. Netherlands Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 117. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 118. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 119. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 120. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 121. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 122. MEA Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2017-2022)
  • Table 123. MEA Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 124. MEA Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 125. MEA Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 126. MEA Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 127. MEA Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 128. Middle East Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 129. Middle East Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 130. Middle East Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 131. Middle East Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 132. Middle East Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 133. Africa Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 134. Africa Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 135. Africa Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 136. Africa Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 137. Africa Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 138. North America Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2017-2022)
  • Table 139. North America Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 140. North America Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 141. North America Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 142. North America Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 143. North America Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 144. United States Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 145. United States Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 146. United States Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 147. United States Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 148. United States Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 149. Canada Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 150. Canada Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 151. Canada Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 152. Canada Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 153. Canada Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 154. Mexico Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2017-2022)
  • Table 155. Mexico Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2017-2022)
  • Table 156. Mexico Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2017-2022)
  • Table 157. Mexico Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2017-2022)
  • Table 158. Mexico Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2017-2022)
  • Table 159. Data Engineering, Preparation, and Labeling for AI: by Type(USD/Units)
  • Table 160. Company Basic Information, Sales Area and Its Competitors
  • Table 161. Company Basic Information, Sales Area and Its Competitors
  • Table 162. Company Basic Information, Sales Area and Its Competitors
  • Table 163. Company Basic Information, Sales Area and Its Competitors
  • Table 164. Company Basic Information, Sales Area and Its Competitors
  • Table 165. Company Basic Information, Sales Area and Its Competitors
  • Table 166. Data Engineering, Preparation, and Labeling for AI: by Type(USD Million)
  • Table 167. Data Engineering, Preparation, and Labeling for AI Data Engineering , by Region USD Million (2023-2028)
  • Table 168. Data Engineering, Preparation, and Labeling for AI Data Preparation , by Region USD Million (2023-2028)
  • Table 169. Data Engineering, Preparation, and Labeling for AI Data Labeling , by Region USD Million (2023-2028)
  • Table 170. Data Engineering, Preparation, and Labeling for AI: by Application(USD Million)
  • Table 171. Data Engineering, Preparation, and Labeling for AI Data Labeling , by Region USD Million (2023-2028)
  • Table 172. Data Engineering, Preparation, and Labeling for AI Data Preparation , by Region USD Million (2023-2028)
  • Table 173. Data Engineering, Preparation, and Labeling for AI AI Applications in Engineering , by Region USD Million (2023-2028)
  • Table 174. Data Engineering, Preparation, and Labeling for AI: by End User Industry(USD Million)
  • Table 175. Data Engineering, Preparation, and Labeling for AI Banking, Financial Services, and Insurance , by Region USD Million (2023-2028)
  • Table 176. Data Engineering, Preparation, and Labeling for AI Healthcare and Pharma , by Region USD Million (2023-2028)
  • Table 177. Data Engineering, Preparation, and Labeling for AI Retail , by Region USD Million (2023-2028)
  • Table 178. Data Engineering, Preparation, and Labeling for AI Technology , by Region USD Million (2023-2028)
  • Table 179. Data Engineering, Preparation, and Labeling for AI Media and Entertainment , by Region USD Million (2023-2028)
  • Table 180. Data Engineering, Preparation, and Labeling for AI Automotive , by Region USD Million (2023-2028)
  • Table 181. Data Engineering, Preparation, and Labeling for AI Transportation , by Region USD Million (2023-2028)
  • Table 182. Data Engineering, Preparation, and Labeling for AI Others , by Region USD Million (2023-2028)
  • Table 183. Data Engineering, Preparation, and Labeling for AI: by Project Type(USD Million)
  • Table 184. Data Engineering, Preparation, and Labeling for AI Internal Development , by Region USD Million (2023-2028)
  • Table 185. Data Engineering, Preparation, and Labeling for AI Third-Party Solution , by Region USD Million (2023-2028)
  • Table 186. Data Engineering, Preparation, and Labeling for AI: by Organisation Size(USD Million)
  • Table 187. Data Engineering, Preparation, and Labeling for AI SMEs , by Region USD Million (2023-2028)
  • Table 188. Data Engineering, Preparation, and Labeling for AI Large Enterprise , by Region USD Million (2023-2028)
  • Table 189. South America Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2023-2028)
  • Table 190. South America Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 191. South America Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 192. South America Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 193. South America Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 194. South America Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 195. Brazil Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 196. Brazil Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 197. Brazil Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 198. Brazil Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 199. Brazil Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 200. Argentina Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 201. Argentina Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 202. Argentina Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 203. Argentina Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 204. Argentina Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 205. Rest of South America Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 206. Rest of South America Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 207. Rest of South America Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 208. Rest of South America Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 209. Rest of South America Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 210. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2023-2028)
  • Table 211. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 212. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 213. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 214. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 215. Asia Pacific Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 216. China Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 217. China Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 218. China Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 219. China Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 220. China Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 221. Japan Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 222. Japan Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 223. Japan Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 224. Japan Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 225. Japan Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 226. India Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 227. India Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 228. India Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 229. India Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 230. India Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 231. South Korea Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 232. South Korea Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 233. South Korea Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 234. South Korea Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 235. South Korea Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 236. Taiwan Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 237. Taiwan Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 238. Taiwan Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 239. Taiwan Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 240. Taiwan Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 241. Australia Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 242. Australia Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 243. Australia Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 244. Australia Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 245. Australia Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 246. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 247. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 248. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 249. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 250. Rest of Asia-Pacific Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 251. Europe Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2023-2028)
  • Table 252. Europe Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 253. Europe Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 254. Europe Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 255. Europe Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 256. Europe Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 257. Germany Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 258. Germany Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 259. Germany Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 260. Germany Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 261. Germany Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 262. France Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 263. France Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 264. France Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 265. France Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 266. France Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 267. Italy Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 268. Italy Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 269. Italy Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 270. Italy Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 271. Italy Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 272. United Kingdom Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 273. United Kingdom Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 274. United Kingdom Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 275. United Kingdom Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 276. United Kingdom Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 277. Netherlands Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 278. Netherlands Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 279. Netherlands Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 280. Netherlands Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 281. Netherlands Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 282. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 283. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 284. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 285. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 286. Rest of Europe Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 287. MEA Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2023-2028)
  • Table 288. MEA Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 289. MEA Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 290. MEA Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 291. MEA Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 292. MEA Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 293. Middle East Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 294. Middle East Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 295. Middle East Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 296. Middle East Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 297. Middle East Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 298. Africa Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 299. Africa Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 300. Africa Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 301. Africa Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 302. Africa Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 303. North America Data Engineering, Preparation, and Labeling for AI, by Country USD Million (2023-2028)
  • Table 304. North America Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 305. North America Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 306. North America Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 307. North America Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 308. North America Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 309. United States Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 310. United States Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 311. United States Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 312. United States Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 313. United States Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 314. Canada Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 315. Canada Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 316. Canada Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 317. Canada Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 318. Canada Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 319. Mexico Data Engineering, Preparation, and Labeling for AI, by Type USD Million (2023-2028)
  • Table 320. Mexico Data Engineering, Preparation, and Labeling for AI, by Application USD Million (2023-2028)
  • Table 321. Mexico Data Engineering, Preparation, and Labeling for AI, by End User Industry USD Million (2023-2028)
  • Table 322. Mexico Data Engineering, Preparation, and Labeling for AI, by Project Type USD Million (2023-2028)
  • Table 323. Mexico Data Engineering, Preparation, and Labeling for AI, by Organisation Size USD Million (2023-2028)
  • Table 324. Data Engineering, Preparation, and Labeling for AI: by Type(USD/Units)
  • Table 325. Research Programs/Design for This Report
  • Table 326. Key Data Information from Secondary Sources
  • Table 327. 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 Engineering, Preparation, and Labeling for AI: by Type USD Million (2017-2022)
  • Figure 5. Global Data Engineering, Preparation, and Labeling for AI: by Application USD Million (2017-2022)
  • Figure 6. Global Data Engineering, Preparation, and Labeling for AI: by End User Industry USD Million (2017-2022)
  • Figure 7. Global Data Engineering, Preparation, and Labeling for AI: by Project Type USD Million (2017-2022)
  • Figure 8. Global Data Engineering, Preparation, and Labeling for AI: by Organisation Size USD Million (2017-2022)
  • Figure 9. South America Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 10. Asia Pacific Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 11. Europe Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 12. MEA Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 13. North America Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 14. Global Data Engineering, Preparation, and Labeling for AI: by Type USD/Units (2017-2022)
  • Figure 15. Global Data Engineering, Preparation, and Labeling for AI share by Players 2022 (%)
  • Figure 16. Global Data Engineering, Preparation, and Labeling for AI share by Players (Top 3) 2022(%)
  • Figure 17. BCG Matrix for key Companies
  • Figure 18. CloudFactory (United Kingdom) Revenue, Net Income and Gross profit
  • Figure 19. CloudFactory (United Kingdom) Revenue: by Geography 2022
  • Figure 20. Figure Eight (United States) Revenue, Net Income and Gross profit
  • Figure 21. Figure Eight (United States) Revenue: by Geography 2022
  • Figure 22. IMerit (India) Revenue, Net Income and Gross profit
  • Figure 23. IMerit (India) Revenue: by Geography 2022
  • Figure 24. Melissa Data (United States) Revenue, Net Income and Gross profit
  • Figure 25. Melissa Data (United States) Revenue: by Geography 2022
  • Figure 26. Paxata (United States) Revenue, Net Income and Gross profit
  • Figure 27. Paxata (United States) Revenue: by Geography 2022
  • Figure 28. Trifacta (United States) Revenue, Net Income and Gross profit
  • Figure 29. Trifacta (United States) Revenue: by Geography 2022
  • Figure 30. Global Data Engineering, Preparation, and Labeling for AI: by Type USD Million (2023-2028)
  • Figure 31. Global Data Engineering, Preparation, and Labeling for AI: by Application USD Million (2023-2028)
  • Figure 32. Global Data Engineering, Preparation, and Labeling for AI: by End User Industry USD Million (2023-2028)
  • Figure 33. Global Data Engineering, Preparation, and Labeling for AI: by Project Type USD Million (2023-2028)
  • Figure 34. Global Data Engineering, Preparation, and Labeling for AI: by Organisation Size USD Million (2023-2028)
  • Figure 35. South America Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 36. Asia Pacific Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 37. Europe Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 38. MEA Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 39. North America Data Engineering, Preparation, and Labeling for AI Share (%), by Country
  • Figure 40. Global Data Engineering, Preparation, and Labeling for AI: by Type USD/Units (2023-2028)
List of companies from research coverage that are profiled in the study
  • CloudFactory (United Kingdom)
  • Figure Eight (United States)
  • iMerit (India)
  • Melissa Data (United States)
  • Paxata (United States)
  • Trifacta (United States)
Additional players considered in the study are as follows:
The MathWorks, Inc. (United States) , Alegion (United States)
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Key Highlights of Report


Dec 2023 170 Pages 69 Tables Base Year: 2022 Coverage: 15+ Companies; 18 Countries

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Global Data Engineering, Preparation, and Labeling for AI Market Quantitative research analysis
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Frequently Asked Questions (FAQ):

Historical year: 2018-2022; Base year: 2022; Forecast period: 2023 to 2028
Companies that are profiled in Global Data Engineering, Preparation, and Labeling for AI Market are CloudFactory (United Kingdom), Figure Eight (United States), iMerit (India), Melissa Data (United States), Paxata (United States) and Trifacta (United States) etc.
Data Labeling segment in Global market to hold robust market share owing to "Proliferation in Data Generation ".
AMA Research predicts that United States Players will contribute to the maximum growth of Global Data Engineering, Preparation, and Labeling for AI market throughout the forecasted period.

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Personal Cloud Storage Comprehensive Study by End users (Enterprises, Individuals), Hosting type (Service providers, Consumers), Industry vertical (Government, Financial institutions, Healthcare, IT ...
$ 3450
AI-Powered Storage Comprehensive Study by Storage Architecture (File- and Object-Based Storage, Object Storage), Storage System (Direct-attached Storage (DAS), Network-attached Storage (NAS), Storage...
$ 3750
High Availability Server  Comprehensive Study by Type (Availability Level 1 (90%), Availability Level 2(99%), Availability Level 3(99.9%), Availability Level 4(99.99%), Availability Level 5(99.999%))...
$ 3650