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市場調查報告書
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2044338

資料中心人工智慧開發市場預測至2034年－按組件、資料類型、部署模式、資料生命週期階段、應用、最終使用者和地區分類的全球分析

Data-Centric AI Development Market Forecasts to 2034 - Global Analysis By Component (Tools & Platforms and Services), Data Type, Deployment Mode, Data Lifecycle Stage, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球以數據為中心的 AI 開發市場將達到 84 億美元，並在預測期內以 18.2% 的複合年成長率成長，到 2034 年達到 321 億美元。

以資料為中心的人工智慧開發是指一種系統性的探究方法，它透過優先考慮訓練資料集的品質、一致性、標註準確性和代表性來提升人工智慧模型的品管，而不僅僅是最佳化模型架構。這種方法利用專用工具調查方法框架、自動化資料品質評估引擎、群眾外包標註管理系統和資料驅動的模型除錯工具，使人工智慧工程師能夠系統地識別和解決資料缺陷，從而提高視覺、語言、語音和結構化預測任務中生產模型的準確性。

實際應用中人工智慧的精度要求

在醫療診斷、自動駕駛、金融詐欺檢測和工業品質檢測等關鍵應用領域部署人工智慧系統，對準確性和可靠性提出了極高的要求。這些要求僅靠模型架構的改進無法實現，只有透過系統化的資料品管才能滿足。部署生產級人工智慧系統的組織體認到，80%的模型效能問題源自於訓練資料的缺陷，而非演算法本身的限制。因此，他們正大力投資於以數據為中心的開發基礎設施，以確保標註品質的一致性，消除系統性的標註錯誤，並保證對各種極端情況的全面覆蓋。

數據標註的成本和規模

為複雜的AI任務（例如醫學影像分割、自動駕駛場景理解和多語言自然語言處理）產生海量精確標註的訓練數據，需要對專業標註員、培訓、品質保證和管理基礎設施進行大量投資。這構成了巨大的成本障礙，限制了小規模組織採用以資料為中心的AI。需要數百萬個高精度標註的企業級AI團隊面臨著標註成本過高的問題，這佔據了其AI開發預算的很大一部分。同時，試圖在大規模分散式標註員網路中保持標註品質的一致性會導致系統性差異，從而削弱以資料為中心的方法旨在實現的資料品質提升。

合成資料生成簡介

生成式人工智慧和模擬技術的進步，使得在現實世界資料擷取成本過高、受隱私限制或出於安全原因無法進行的情況下，能夠創建高精度的合成訓練資料成為可能。這為以數據為中心的人工智慧開發平台供應商提供了一個變革性的機遇，使其目標市場從標註服務擴展到整合的數據生成和管理解決方案。利用合成感測器數據的汽車人工智慧開發商、產生符合隱私規定的合成患者記錄的醫療人工智慧公司以及模擬極端情況的機器人公司，都在推動著可直接與數據品管基礎設施整合的合成數據平台的快速普及。

自動機器學習和基礎模型

大規模基礎模型的快速發展，使得許多企業級人工智慧應用所需的客製化訓練資料量大幅減少，而這些應用正是以資料為中心的人工智慧開發平台的主要收入來源，並對支撐這些平台收入的大規模資料標註和品管服務構成了威脅。如果基礎模型的遷移學習能力進一步提升，企業級人工智慧應用只需要幾百個高品質樣本而非數百萬個標註樣本，那麼主流人工智慧應用情境對大規模資料中心開發基礎設施的結構性需求可能會顯著下降。

新冠疫情的影響：

疫情大大加速了企業人工智慧在遠距辦公、電子商務、醫​​療診斷和供應鏈管理等領域的應用，從而提升了對生產級人工智慧系統的需求，而這些系統需要強大的訓練資料基礎設施。遠距辦公的需求推動了分散式標註人才管理平台的快速發展，實現了全球資料標註作業。疫情結束後，企業人工智慧已日趨成熟，採用以資料為中心的調查方法不再只是可選項，而是出於生產部署對品質和合規性的要求，成為一項策略性必然選擇。

在預測期內，服務業預計將佔據最大的市場佔有率。

預計在預測期內，服務領域將佔據最大的市場佔有率。這主要歸功於專家知識的附加價值，這些知識能夠指導企業組織設計資料策略、建立標註工作流程架構以及部署生產級人工智慧。許多企業內部團隊如果沒有外部支持，往往難以具備這種專業知識。大型企業在進行策略性人工智慧轉型專案時，需要涵蓋資料管治框架、標註供應商選擇、品質保證協議設計以及人工智慧模型審計等方面的綜合諮詢契約，這些服務正在為專業服務領域創造可觀的收入。領先的顧問公司和專業的人工智慧服務公司正在擴展其以數據為中心的人工智慧業務，以滿足企業的需求。

在預測期內，結構化資料區段預計將呈現最高的複合年成長率。

在預測期內，結構化資料區段預計將呈現最高的成長率，這主要得益於企業人工智慧在金融服務、醫療記錄管理、供應鏈最佳化和客戶分析等領域的顯著應用。在這些領域，結構化表格形式資料和交易資料正被用作主要的訓練輸入。部署人工智慧驅動的詐欺偵測、信用風險管理和交易系統的金融機構正在大力投資結構化資料品管基礎設施，以滿足監管模型檢驗的要求。雲端資料倉儲的普及正在將企業級資料管道的品管集中化，從而加速利用結構化資料的人工智慧開發。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於其高度集中的企業人工智慧開發活動、領先的人工智慧研究機構以及眾多獲得大量創業投資投資的資料中心平台新創公司。美國擁有最大的人工智慧開發工具公司生態系統，其中包括Scale AI、Labelbox和Weights & Biases等，這些公司正在建立全面的資料中心開發基礎設施。谷歌、微軟和亞馬遜等企業科技公司正大力投資資料品質和管理工具，並將其整合到自身的人工智慧開發雲端平台。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率。這主要得益於中國、印度、韓國和日本企業人工智慧的加速應用，以及各國政府推動人工智慧發展、加強國內人工智慧能力建設的各項計劃，這些因素共同催生了對以數據為中心的開發平台的顯著需求。中國的國家人工智慧戰略正在推動製造業、醫療保健和金融服務業大規模應用人工智慧，從而產生了對訓練資料產生的巨大需求。在印度，人工智慧服務出口產業的蓬勃發展以及國內數位轉型（DX）計畫的推進，正推動著對數據標註和品管平台的強勁投資。

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  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
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    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球資料中心人工智慧開發市場：按組件分類

  • 工具和平台
    • 數據標註工具
    • 數據版本管理平台
    • 數據品管工具
  • 服務
    • 數據標註服務
    • 人工智慧諮詢服務
    • 數據工程服務

第6章：全球資料中心人工智慧開發市場：按資料類型分類

  • 結構化資料
  • 非結構化數據
    • 文字數據
    • 影像資料
    • 影片數據
  • 半結構化數據

第7章：全球資料中心人工智慧開發市場：按部署模式分類

  • 現場
  • 基於雲端的
  • 混合實現

第8章：全球資料中心人工智慧開發市場：按資料生命週期階段分類

  • 數據收集
  • 資料清洗和預處理
  • 數據標註和註釋
  • 模型訓練與最佳化

第9章：全球資料中心人工智慧開發市場：按應用領域分類

  • 自然語言處理
  • 電腦視覺
  • 語音辨識
  • 建議​​統
  • 詐欺偵測

第10章：全球資料中心人工智慧開發市場：按最終用戶分類

  • 公司
  • 人工智慧新創公司
  • 研究機構

第11章：全球資料中心人工智慧開發市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • Google LLC
  • Microsoft Corporation
  • Amazon Web Services Inc.
  • IBM Corporation
  • Snowflake Inc.
  • Databricks Inc.
  • Scale AI Inc.
  • Appen Limited
  • Samasource Inc.
  • Alteryx Inc.
  • DataRobot Inc.
  • H2O.ai Inc.
  • Oracle Corporation
  • SAP SE
  • Cloudera Inc.
  • Teradata Corporation
  • C3.ai Inc.
Product Code: SMRC36127

According to Stratistics MRC, the Global Data-Centric AI Development Market is accounted for $8.4 billion in 2026 and is expected to reach $32.1 billion by 2034 growing at a CAGR of 18.2% during the forecast period. Data-centric AI development refers to the systematic methodology of improving artificial intelligence model performance by prioritizing the quality, consistency, labeling accuracy, and representativeness of training datasets over model architecture optimization alone, supported by specialized tooling platforms for data collection, cleaning, annotation, versioning, and quality management throughout the AI development lifecycle. These platforms incorporate active learning frameworks, automated data quality assessment engines, crowdsourced annotation management systems, and data-driven model debugging tools that enable AI engineers to systematically identify and resolve data defects that limit production model accuracy across vision, language, speech, and structured prediction tasks.

Market Dynamics:

Driver:

Production AI accuracy demands

Enterprise deployment of AI systems in high-stakes applications, including medical diagnosis, autonomous vehicle control, financial fraud detection, and industrial quality inspection, is generating rigorous accuracy and reliability requirements that can only be achieved through systematic data quality management rather than model architecture improvements alone. Organizations deploying production AI systems are discovering that 80 percent of model performance problems originate in training data defects rather than algorithmic limitations, driving systematic investment in data-centric development infrastructure that guarantees consistent annotation quality, eliminates systematic labeling errors, and ensures comprehensive edge case coverage.

Restraint:

Data annotation cost and scale

Producing large volumes of accurately labeled training data for complex AI tasks, including medical image segmentation, autonomous driving scene understanding, and multi-language NLP, requires substantial investment in specialized annotator recruitment, training, quality assurance, and management infrastructure that creates significant cost barriers limiting data-centric AI adoption among smaller organizations. Enterprise AI teams requiring millions of high-precision annotations face annotation cost structures that consume disproportionate shares of AI development budgets, while maintaining annotation quality consistency across large distributed annotator workforces introduces systematic variance that undermines the data quality improvements that data-centric approaches are designed to achieve.

Opportunity:

Synthetic data generation adoption

Advances in generative AI and simulation technology enabling high-fidelity synthetic training data generation for scenarios where real-world data collection is prohibitively expensive, privacy-restricted, or safety-prohibitive represent a transformative opportunity for data-centric AI development platform vendors to expand addressable markets beyond annotation services into integrated data generation and management solutions. Automotive AI developers using synthetic sensor data, healthcare AI companies generating synthetic patient records compliant with privacy regulations, and robotics firms simulating edge case scenarios are driving rapid adoption of synthetic data platforms that integrate directly with data quality management infrastructure.

Threat:

AutoML and foundation models

Rapid advancement of large foundation models pre-trained on internet-scale datasets that achieve strong performance on downstream tasks with minimal fine-tuning data is potentially reducing the volume of custom training data required for many enterprise AI applications, threatening the demand for large-scale data annotation and quality management services that underpin data-centric AI development platform revenue. If foundation model transfer learning capabilities continue improving to the point where enterprise AI applications require only hundreds of high-quality examples rather than millions of annotated samples, the structural demand for extensive data-centric development infrastructure may decline significantly across mainstream AI use cases.

Covid-19 Impact:

The pandemic dramatically accelerated enterprise AI adoption across remote work, e-commerce, healthcare diagnostics, and supply chain management, which intensified demand for production-quality AI systems requiring rigorous training data infrastructure. Remote work requirements drove the rapid development of distributed annotation workforce management platforms, enabling global data labeling operations. Post-pandemic, enterprise AI maturity has advanced to the stage where production deployment quality and regulatory compliance requirements make data-centric development methodology adoption a strategic necessity rather than an optional best practice.

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

The services segment is expected to account for the largest market share during the forecast period, due to the premium value of specialized expertise guiding enterprise organizations through data strategy design, annotation workflow architecture, and production AI deployment that most internal teams lack without external support. Large enterprises undertaking strategic AI transformation programs require comprehensive consulting engagements covering data governance frameworks, annotation vendor selection, quality assurance protocol design, and AI model auditing that generate substantial professional services revenue. Major consulting firms and specialized AI services companies are scaling data-centric AI practices to meet enterprise demand.

The structured data segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the structured data segment is predicted to witness the highest growth rate, driven by the massive expansion of enterprise AI applications in financial services, healthcare records management, supply chain optimization, and customer analytics that rely on structured tabular and transactional data as the primary training input. Financial institutions deploying AI fraud detection, credit risk, and trading systems are investing heavily in structured data quality management infrastructure to meet regulatory model validation requirements. The proliferation of cloud data warehouses is accelerating structured data AI development by centralizing quality management across enterprise data pipelines.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the world's highest concentration of enterprise AI development activity, leading AI research institutions, and data-centric platform startups receiving significant venture capital investment. The United States hosts the largest ecosystem of AI development tooling companies, including Scale AI, Labelbox, and Weights & Biases, that are building a comprehensive data-centric development infrastructure. Enterprise technology companies, including Google, Microsoft, and Amazon, are making substantial investments in data quality and management tooling integrated with their AI development cloud platforms.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is expected to exhibit the highest CAGR, driven by the acceleration of enterprise AI adoption in China, India, South Korea, and Japan, combined with government AI development programs that mandate domestic AI capability building, generating substantial institutional demand for data-centric development platforms. China's national AI strategy, which is driving large-scale AI deployment in manufacturing, healthcare, and financial services, is creating enormous training data production requirements. India's growing AI services export industry and domestic digital transformation programs are driving strong investment in data annotation and quality management platforms.

Key players in the market

Google LLC, Microsoft Corporation, Amazon Web Services Inc., IBM Corporation, Snowflake Inc., Databricks Inc., Scale AI Inc., Appen Limited, Samasource Inc., Alteryx Inc., DataRobot Inc., H2O.ai Inc., Oracle Corporation, SAP SE, Cloudera Inc., Teradata Corporation, and C3.ai Inc..

Key Developments:

In April 2026, Databricks Inc. expanded its Mosaic AI platform with data-centric model evaluation tools enabling systematic identification and remediation of training data quality issues in large language model fine-tuning pipelines.

In February 2026, Snorkel AI Inc. announced a major enterprise partnership with a leading healthcare provider to deploy programmatic data labeling infrastructure for clinical AI model development across radiology and pathology applications.

In January 2026, Labelbox Inc. introduced integrated synthetic data generation capabilities within its data-centric AI platform, enabling seamless blending of real and synthetic training examples for improved model robustness.

Solution Types Covered:

  • Carbon Monitoring Platforms
  • AI-Based Soil Analytics
  • Carbon Credit Platforms
  • MRV (Measurement Reporting Verification) Tools
  • Predictive Carbon Modeling Systems
  • Soil Data Intelligence Platforms

Farm Types Covered:

  • Row Crop Farms
  • Permanent Crop Farms
  • Mixed Farms
  • Agroforestry Systems

Technologies Covered:

  • Machine Learning Models
  • Remote Sensing & Satellite Analytics
  • IoT Soil Sensors
  • Big Data Platforms
  • Blockchain for Carbon Credits

Applications Covered:

  • Carbon Credit Generation
  • Soil Health Monitoring
  • Sustainable Farming Planning
  • Climate Reporting
  • Regenerative Agriculture

End Users Covered:

  • Farmers
  • Agribusiness Companies
  • Carbon Credit Developers
  • Government Organizations

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global Data-Centric AI Development Market, By Component

  • 5.1 Tools & Platforms
    • 5.1.1 Data Labeling Tools
    • 5.1.2 Data Versioning Platforms
    • 5.1.3 Data Quality Management Tools
  • 5.2 Services
    • 5.2.1 Data Annotation Services
    • 5.2.2 AI Consulting Services
    • 5.2.3 Data Engineering Services

6 Global Data-Centric AI Development Market, By Data Type

  • 6.1 Structured Data
  • 6.2 Unstructured Data
    • 6.2.1 Text Data
    • 6.2.2 Image Data
    • 6.2.3 Video Data
  • 6.3 Semi-Structured Data

7 Global Data-Centric AI Development Market, By Deployment Mode

  • 7.1 On-Premises
  • 7.2 Cloud-Based
  • 7.3 Hybrid Deployment

8 Global Data-Centric AI Development Market, By Data Lifecycle Stage

  • 8.1 Data Collection
  • 8.2 Data Cleaning & Preparation
  • 8.3 Data Labeling & Annotation
  • 8.4 Model Training & Optimization

9 Global Data-Centric AI Development Market, By Application

  • 9.1 Natural Language Processing
  • 9.2 Computer Vision
  • 9.3 Speech Recognition
  • 9.4 Recommendation Systems
  • 9.5 Fraud Detection

10 Global Data-Centric AI Development Market, By End User

  • 10.1 Enterprises
  • 10.2 AI Startups
  • 10.3 Research Institutions

11 Global Data-Centric AI Development Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 Google LLC
  • 14.2 Microsoft Corporation
  • 14.3 Amazon Web Services Inc.
  • 14.4 IBM Corporation
  • 14.5 Snowflake Inc.
  • 14.6 Databricks Inc.
  • 14.7 Scale AI Inc.
  • 14.8 Appen Limited
  • 14.9 Samasource Inc.
  • 14.10 Alteryx Inc.
  • 14.11 DataRobot Inc.
  • 14.12 H2O.ai Inc.
  • 14.13 Oracle Corporation
  • 14.14 SAP SE
  • 14.15 Cloudera Inc.
  • 14.16 Teradata Corporation
  • 14.17 C3.ai Inc.

List of Tables

  • Table 1 Global Data-Centric AI Development Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Data-Centric AI Development Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global Data-Centric AI Development Market Outlook, By Tools & Platforms (2023-2034) ($MN)
  • Table 4 Global Data-Centric AI Development Market Outlook, By Data Labeling Tools (2023-2034) ($MN)
  • Table 5 Global Data-Centric AI Development Market Outlook, By Data Versioning Platforms (2023-2034) ($MN)
  • Table 6 Global Data-Centric AI Development Market Outlook, By Data Quality Management Tools (2023-2034) ($MN)
  • Table 7 Global Data-Centric AI Development Market Outlook, By Services (2023-2034) ($MN)
  • Table 8 Global Data-Centric AI Development Market Outlook, By Data Annotation Services (2023-2034) ($MN)
  • Table 9 Global Data-Centric AI Development Market Outlook, By AI Consulting Services (2023-2034) ($MN)
  • Table 10 Global Data-Centric AI Development Market Outlook, By Data Engineering Services (2023-2034) ($MN)
  • Table 11 Global Data-Centric AI Development Market Outlook, By Data Type (2023-2034) ($MN)
  • Table 12 Global Data-Centric AI Development Market Outlook, By Structured Data (2023-2034) ($MN)
  • Table 13 Global Data-Centric AI Development Market Outlook, By Unstructured Data (2023-2034) ($MN)
  • Table 14 Global Data-Centric AI Development Market Outlook, By Text Data (2023-2034) ($MN)
  • Table 15 Global Data-Centric AI Development Market Outlook, By Image Data (2023-2034) ($MN)
  • Table 16 Global Data-Centric AI Development Market Outlook, By Video Data (2023-2034) ($MN)
  • Table 17 Global Data-Centric AI Development Market Outlook, By Semi-Structured Data (2023-2034) ($MN)
  • Table 18 Global Data-Centric AI Development Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 19 Global Data-Centric AI Development Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 20 Global Data-Centric AI Development Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 21 Global Data-Centric AI Development Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 22 Global Data-Centric AI Development Market Outlook, By Data Lifecycle Stage (2023-2034) ($MN)
  • Table 23 Global Data-Centric AI Development Market Outlook, By Data Collection (2023-2034) ($MN)
  • Table 24 Global Data-Centric AI Development Market Outlook, By Data Cleaning & Preparation (2023-2034) ($MN)
  • Table 25 Global Data-Centric AI Development Market Outlook, By Data Labeling & Annotation (2023-2034) ($MN)
  • Table 26 Global Data-Centric AI Development Market Outlook, By Model Training & Optimization (2023-2034) ($MN)
  • Table 27 Global Data-Centric AI Development Market Outlook, By Application (2023-2034) ($MN)
  • Table 28 Global Data-Centric AI Development Market Outlook, By Natural Language Processing (2023-2034) ($MN)
  • Table 29 Global Data-Centric AI Development Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 30 Global Data-Centric AI Development Market Outlook, By Speech Recognition (2023-2034) ($MN)
  • Table 31 Global Data-Centric AI Development Market Outlook, By Recommendation Systems (2023-2034) ($MN)
  • Table 32 Global Data-Centric AI Development Market Outlook, By Fraud Detection (2023-2034) ($MN)
  • Table 33 Global Data-Centric AI Development Market Outlook, By End User (2023-2034) ($MN)
  • Table 34 Global Data-Centric AI Development Market Outlook, By Enterprises (2023-2034) ($MN)
  • Table 35 Global Data-Centric AI Development Market Outlook, By AI Startups (2023-2034) ($MN)
  • Table 36 Global Data-Centric AI Development Market Outlook, By Research Institutions (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.