查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1953397

資料科學平台市場 - 全球產業規模、佔有率、趨勢、機會及預測（按部署方式、公司類型、應用、產業、地區和競爭格局分類），2021-2031年

Data Science Platform Market - Global Industry Size, Share, Trends, Opportunity, and Forecast Segmented By Deployment, By Enterprise Type, By Application, By Industry, By Region & Competition, 2021-2031F
出版日期: | 出版商: TechSci Research | 英文 185 Pages | 商品交期: 2-3個工作天內

價格

We offer 8 hour analyst time for an additional research. Please contact us for the details.

簡介目錄

全球資料科學平台市場預計將從 2025 年的 585.3 億美元成長到 2031 年的 2,255.3 億美元，複合年成長率達到 25.21%。

這些平台作為整合軟體基礎設施，支援從資料準備和模型訓練到最終部署和持續監控的整個分析生命週期。關鍵成長要素包括人工智慧營運需求的日益成長，以及對能夠最佳化工程團隊和相關人員之間工作流程的協作生態系統的需求。此外，在管理大規模資料集時對集中控制和可重現性的需求，也持續推動該領域在國際工業界的穩定成長。

市場概覽
預測期 2027-2031
市場規模：2025年 585.3億美元
市場規模：2031年 2255.3億美元
複合年成長率：2026-2031年 25.21%
成長最快的細分市場 客戶支援
最大的市場 北美洲

儘管市場規模不斷擴大，但能夠有效運作這些複雜生態系統的專業人才嚴重短缺，阻礙了市場發展。企業在招募具備統計和技術技能的人才方面常常面臨挑戰，而這些技能對於有效利用這些工具至關重要，從而導致實施瓶頸。根據電腦產業協會 (CompTIA) 的數據，預計到 2024 年，數據科學家和分析師的就業成長率將達到 5.5%，但這一需求遠遠超過了合格人才的供應。這種日益擴大的技能缺口增加了實施策略的複雜性，並延緩了企業實現投資回報的時間。

市場促進因素

人工智慧 (AI) 和機器學習技術的快速普及推動了對強大營運基礎設施的需求，並將資料科學平台確立為企業不可或缺的資源。隨著企業從實驗階段過渡到全面應用，它們在模型管治、可擴展性和生命週期管理方面面臨著許多複雜挑戰——而整合平台正是為應對這些挑戰而專門建構的。根據 IBM 統計，截至 2024 年 1 月，約 42% 的企業級組織已將 AI 積極整合到其營運中，這對支援如此廣泛應用的系統提出了巨大的要求。因此，平台正在不斷發展，以最佳化從開發到生產的流程，並確保分析投資能帶來可衡量的成果。 Databricks 發布的《2024 年數據與 AI 現況報告》也印證了這一點，該報告指出，與前一年相比，已部署到生產環境中的 AI 模型數量成長了 11 倍。

同時，資料科學正日益普及，市場進入不再侷限於專業工程團隊，公民資料科學科學家也逐漸成為主流。為了平衡技術複雜性與業務效用，供應商正在加速採用低程式碼/無程式碼介面，使非技術相關人員能夠直接參與分析工作流程。這種轉變最大限度地減少了瓶頸，並在整個組織內培育以數據為中心的文化。根據Google雲端於2024年3月發布的《2024年數據與人工智慧趨勢報告》，約三分之二的數據決策者希望全年都能更便捷地獲取洞察，這主要得益於生成式人工智慧能力的提升。透過向更廣泛的員工群體提供先進的分析工具，資料科學平台能夠幫助企業擴展決策能力，並最佳化數據投資盈利。

市場挑戰

熟練的專業人才嚴重短缺是限制全球資料科學平台市場成長的主要障礙。隨著企業採用更先進的軟體基礎設施來運作人工智慧和機器學習，它們日益面臨能夠管理這些複雜生態系統的人才短缺問題。由於缺乏必要的人力資本來監督技術工作流程，企業難以將原始數據轉化為可執行的洞察，造成嚴重的實施瓶頸。因此，企業面臨計劃延期和實施計畫停滯不前的問題，直接導致預期投資收益的實現延遲。

近期來自供給面的統計數據凸顯了日益擴大的技能缺口的嚴重性：根據美國統計協會的數據，到2024年，資料科學碩士課程每年將培養約2400名畢業生，這一數字遠遠不足以滿足行業快速成長的需求。合格人才的短缺導致企業間對數量有限的專業人才展開激烈競爭，造成營運摩擦，阻礙了資料科學平台的廣泛應用和高效利用。

市場趨勢

隨著企業面臨日益成長的監管壓力以及黑箱演算法帶來的風險，符合倫理的人工智慧管治和可解釋性框架的重要性日益凸顯。隨著資料科學從實驗計劃走向核心業務運營，平台越來越需要整合嚴格的監督機制，以確保演算法決策的透明度、公平性和課責。這一趨勢源自於彌合快速技術應用與組織管理相關風險能力之間差距的迫切需求。思科於2024年12月發布的《2024年人工智慧就緒指數》顯示，僅有31%的組織表示已全面落實人工智慧政策，凸顯了市場對能夠提供整合管治解決方案、滿足複雜合規要求的平台的迫切需求。

同時，生成式人工智慧與合成資料能力的融合正在變革平台架構，從而促進高階人工智慧應用的創建。供應商正迅速採用向量搜尋和搜尋增強生成（RAG）管道，將其平台發展成為建立和管理大規模語言模型（LLM）工作流程的強大引擎。這項技術進步使資料團隊能夠基於自身企業資料建立生成模型，在確保安全性的同時提高準確性和相關性。這種變革的規模體現在採用率數據中。根據Databricks於2024年3月發布的《2024年數據與人工智慧現況報告》，該公司生態系統內的向量資料庫使用量年增377%，凸顯了基礎設施向支援高階生成式人工智慧開發的重大轉變。

目錄

第1章概述

第2章調查方法

第3章執行摘要

第4章：客戶評價

第5章 全球資料科學平台市場展望

  • 市場規模及預測
    • 按金額
  • 市佔率及預測
    • 按部署類型（雲端/本地部署）
    • 按公司規模（大型公司、中小企業）
    • 按應用領域分類（客戶支援、業務營運、行銷、財務/會計、物流等）
    • 按行業分類（銀行、金融、保險、IT、通訊、醫療保健、零售、製造、運輸、其他）
    • 按地區
    • 按公司（2025 年）
  • 市場地圖

第6章：北美資料科學平台市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 北美洲：國家分析
    • 美國
    • 加拿大
    • 墨西哥

7. 歐洲資料科學平台市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 歐洲：國家分析
    • 德國
    • 法國
    • 英國
    • 義大利
    • 西班牙

8. 亞太地區資料科學平台市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲

9. 中東和非洲資料科學平台市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 中東和非洲：國家分析
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非

10. 南美洲資料科學平台市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 南美洲：國家分析
    • 巴西
    • 哥倫比亞
    • 阿根廷

第11章 市場動態

  • 促進要素
  • 任務

第12章 市場趨勢與發展

  • 併購
  • 產品發布
  • 最新進展

第13章 全球資料科學平台市場：SWOT分析

第14章：波特五力分析

  • 產業競爭
  • 新進入者的可能性
  • 供應商電力
  • 顧客權力
  • 替代品的威脅

第15章 競爭格局

  • IBM Corporation
  • Google LLC
  • Microsoft Corporation
  • SAS Institute Inc.
  • Alteryx Inc.
  • Oracle Corporation
  • SAP SE
  • RapidMiner Inc.
  • Dataiku Inc.
  • Databricks Inc.

第16章 策略建議

第17章：關於研究公司及免責聲明

簡介目錄
Product Code: 23073

The Global Data Science Platform Market is projected to expand from USD 58.53 Billion in 2025 to USD 225.53 Billion by 2031, achieving a CAGR of 25.21%. These platforms function as a unified software infrastructure that supports the full analytical lifecycle, ranging from data preparation and model training to final deployment and ongoing monitoring. Primary growth drivers include the rising need to operationalize artificial intelligence and the demand for collaborative ecosystems that optimize workflows between engineering teams and business stakeholders. Furthermore, the necessity for centralized governance and reproducibility when managing massive datasets continues to underpin the sector's steady growth across international industries.

Market Overview
Forecast Period2027-2031
Market Size 2025USD 58.53 Billion
Market Size 2031USD 225.53 Billion
CAGR 2026-203125.21%
Fastest Growing SegmentCustomer Support
Largest MarketNorth America

Despite this expansion, market progress is impeded by a severe shortage of skilled professionals equipped to navigate these intricate ecosystems. Organizations frequently face challenges in recruiting talent with the requisite statistical and technical proficiency to utilize these tools effectively, resulting in adoption bottlenecks. Data from the Computing Technology Industry Association (CompTIA) indicates that while employment for data scientists and analysts was forecast to rise by 5.5% in 2024, this demand significantly exceeds the available supply of qualified candidates. This expanding skills gap creates complications for implementation strategies and postpones the realization of investment returns for enterprises.

Market Driver

The rapid adoption of artificial intelligence and machine learning technologies is intensifying the need for resilient operational infrastructure, establishing data science platforms as essential enterprise resources. As companies move from experimental stages to full-scale implementation, they encounter intricate hurdles regarding model governance, scalability, and lifecycle management that unified platforms are built to resolve. According to IBM, roughly 42% of enterprise-level organizations had actively integrated AI into their operations by January 2024, generating substantial demand for systems capable of supporting such widespread adoption. Consequently, platforms are adapting to optimize the trajectory from development to production, ensuring that analytics investments deliver measurable outcomes; this is reinforced by Databricks' '2024 State of Data + AI Report', which noted an 11-fold increase in production-deployed AI models compared to the previous year.

Concurrently, the rising democratization of data science is extending market accessibility beyond specialized engineering groups to include citizen data scientists. To reconcile technical complexity with business utility, vendors are increasingly incorporating low-code and no-code interfaces that allow non-technical stakeholders to engage directly in analytical workflows. This transition minimizes bottlenecks and promotes a data-centric culture throughout the organization. In its 'Data and AI Trends Report 2024' from March 2024, Google Cloud reported that nearly two-thirds of data decision-makers anticipated democratized access to insights during the year, largely fueled by generative AI capabilities. By offering sophisticated analytical tools to a wider workforce, data science platforms empower enterprises to expand their decision-making capacity and optimize the return on data investments.

Market Challenge

A significant scarcity of skilled professionals serves as a major obstacle to the growth of the Global Data Science Platform Market. As enterprises increasingly deploy advanced software infrastructures to operationalize artificial intelligence and machine learning, they often face a shortage of talent equipped to manage these sophisticated ecosystems. This lack of expertise results in substantial implementation bottlenecks, as organizations struggle to convert raw data into actionable insights without the necessary human capital to oversee technical workflows. Consequently, businesses encounter prolonged project timelines and stalled deployment initiatives, which directly postpones the achievement of expected returns on investment.

The gravity of this expanding skills gap is highlighted by recent supply-side statistics. Data from the American Statistical Association indicates that master's programs in data science produced approximately 2,400 graduates annually in 2024, a number that fails to meet the industry's rapidly growing demands. This restricted pipeline of qualified candidates compels enterprises to compete fiercely for a limited number of experts, generating operational friction that impedes the widespread adoption and efficient application of data science platforms.

Market Trends

The emphasis on Ethical AI Governance and Explainability Frameworks is growing as enterprises confront mounting regulatory pressures and the risks associated with black-box algorithms. As data science transitions from experimental projects to essential business operations, platforms are increasingly required to incorporate strict oversight mechanisms that guarantee transparency, fairness, and accountability in algorithmic decision-making. This trend stems from the critical need to close the divide between rapid technological adoption and an organization's ability to manage related risks. According to Cisco's '2024 AI Readiness Index' from December 2024, only 31% of organizations claimed to have fully comprehensive AI policies in place, underscoring the urgent market demand for platforms providing integrated governance solutions to handle complex compliance requirements.

At the same time, the integration of Generative AI and Synthetic Data Capabilities is transforming platform architectures to facilitate the creation of advanced AI applications. Vendors are swiftly adopting vector search and Retrieval-Augmented Generation (RAG) pipelines, evolving their platforms into robust engines for constructing and managing Large Language Model (LLM) workflows. This technical advancement enables data teams to anchor generative models in proprietary enterprise data, improving accuracy and relevance while maintaining security. The magnitude of this shift is reflected in adoption data; Databricks' '2024 State of Data + AI Report' from March 2024 reveals that usage of vector databases within their ecosystem surged by 377% over the prior year, highlighting a significant transition toward infrastructure capable of supporting advanced generative AI development.

Key Market Players

  • IBM Corporation
  • Google LLC
  • Microsoft Corporation
  • SAS Institute Inc.
  • Alteryx Inc.
  • Oracle Corporation
  • SAP SE
  • RapidMiner Inc.
  • Dataiku Inc.
  • Databricks Inc.

Report Scope

In this report, the Global Data Science Platform Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Data Science Platform Market, By Deployment

  • Cloud
  • On-premise

Data Science Platform Market, By Enterprise Type

  • Large Enterprises
  • Small & Medium Enterprises

Data Science Platform Market, By Application

  • Customer Support
  • Business Operation
  • Marketing
  • Finance & Accounting
  • Logistics
  • Others

Data Science Platform Market, By Industry

  • BFSI
  • IT & Telecom
  • Healthcare
  • Retail
  • Manufacturing
  • Transportation
  • Others

Data Science Platform Market, By Region

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • France
    • United Kingdom
    • Italy
    • Germany
    • Spain
  • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
  • South America
    • Brazil
    • Argentina
    • Colombia
  • Middle East & Africa
    • South Africa
    • Saudi Arabia
    • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Data Science Platform Market.

Available Customizations:

Global Data Science Platform Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

  • 3.1. Overview of the Market
  • 3.2. Overview of Key Market Segmentations
  • 3.3. Overview of Key Market Players
  • 3.4. Overview of Key Regions/Countries
  • 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Data Science Platform Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Deployment (Cloud, On-premise)
    • 5.2.2. By Enterprise Type (Large Enterprises, Small & Medium Enterprises)
    • 5.2.3. By Application (Customer Support, Business Operation, Marketing, Finance & Accounting, Logistics, Others)
    • 5.2.4. By Industry (BFSI, IT & Telecom, Healthcare, Retail, Manufacturing, Transportation, Others)
    • 5.2.5. By Region
    • 5.2.6. By Company (2025)
  • 5.3. Market Map

6. North America Data Science Platform Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Deployment
    • 6.2.2. By Enterprise Type
    • 6.2.3. By Application
    • 6.2.4. By Industry
    • 6.2.5. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Data Science Platform Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Deployment
        • 6.3.1.2.2. By Enterprise Type
        • 6.3.1.2.3. By Application
        • 6.3.1.2.4. By Industry
    • 6.3.2. Canada Data Science Platform Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Deployment
        • 6.3.2.2.2. By Enterprise Type
        • 6.3.2.2.3. By Application
        • 6.3.2.2.4. By Industry
    • 6.3.3. Mexico Data Science Platform Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Deployment
        • 6.3.3.2.2. By Enterprise Type
        • 6.3.3.2.3. By Application
        • 6.3.3.2.4. By Industry

7. Europe Data Science Platform Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Deployment
    • 7.2.2. By Enterprise Type
    • 7.2.3. By Application
    • 7.2.4. By Industry
    • 7.2.5. By Country
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany Data Science Platform Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Deployment
        • 7.3.1.2.2. By Enterprise Type
        • 7.3.1.2.3. By Application
        • 7.3.1.2.4. By Industry
    • 7.3.2. France Data Science Platform Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Deployment
        • 7.3.2.2.2. By Enterprise Type
        • 7.3.2.2.3. By Application
        • 7.3.2.2.4. By Industry
    • 7.3.3. United Kingdom Data Science Platform Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Deployment
        • 7.3.3.2.2. By Enterprise Type
        • 7.3.3.2.3. By Application
        • 7.3.3.2.4. By Industry
    • 7.3.4. Italy Data Science Platform Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Deployment
        • 7.3.4.2.2. By Enterprise Type
        • 7.3.4.2.3. By Application
        • 7.3.4.2.4. By Industry
    • 7.3.5. Spain Data Science Platform Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Deployment
        • 7.3.5.2.2. By Enterprise Type
        • 7.3.5.2.3. By Application
        • 7.3.5.2.4. By Industry

8. Asia Pacific Data Science Platform Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Deployment
    • 8.2.2. By Enterprise Type
    • 8.2.3. By Application
    • 8.2.4. By Industry
    • 8.2.5. By Country
  • 8.3. Asia Pacific: Country Analysis
    • 8.3.1. China Data Science Platform Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Deployment
        • 8.3.1.2.2. By Enterprise Type
        • 8.3.1.2.3. By Application
        • 8.3.1.2.4. By Industry
    • 8.3.2. India Data Science Platform Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Deployment
        • 8.3.2.2.2. By Enterprise Type
        • 8.3.2.2.3. By Application
        • 8.3.2.2.4. By Industry
    • 8.3.3. Japan Data Science Platform Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Deployment
        • 8.3.3.2.2. By Enterprise Type
        • 8.3.3.2.3. By Application
        • 8.3.3.2.4. By Industry
    • 8.3.4. South Korea Data Science Platform Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Deployment
        • 8.3.4.2.2. By Enterprise Type
        • 8.3.4.2.3. By Application
        • 8.3.4.2.4. By Industry
    • 8.3.5. Australia Data Science Platform Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Deployment
        • 8.3.5.2.2. By Enterprise Type
        • 8.3.5.2.3. By Application
        • 8.3.5.2.4. By Industry

9. Middle East & Africa Data Science Platform Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Deployment
    • 9.2.2. By Enterprise Type
    • 9.2.3. By Application
    • 9.2.4. By Industry
    • 9.2.5. By Country
  • 9.3. Middle East & Africa: Country Analysis
    • 9.3.1. Saudi Arabia Data Science Platform Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Deployment
        • 9.3.1.2.2. By Enterprise Type
        • 9.3.1.2.3. By Application
        • 9.3.1.2.4. By Industry
    • 9.3.2. UAE Data Science Platform Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Deployment
        • 9.3.2.2.2. By Enterprise Type
        • 9.3.2.2.3. By Application
        • 9.3.2.2.4. By Industry
    • 9.3.3. South Africa Data Science Platform Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Deployment
        • 9.3.3.2.2. By Enterprise Type
        • 9.3.3.2.3. By Application
        • 9.3.3.2.4. By Industry

10. South America Data Science Platform Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Deployment
    • 10.2.2. By Enterprise Type
    • 10.2.3. By Application
    • 10.2.4. By Industry
    • 10.2.5. By Country
  • 10.3. South America: Country Analysis
    • 10.3.1. Brazil Data Science Platform Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Deployment
        • 10.3.1.2.2. By Enterprise Type
        • 10.3.1.2.3. By Application
        • 10.3.1.2.4. By Industry
    • 10.3.2. Colombia Data Science Platform Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Deployment
        • 10.3.2.2.2. By Enterprise Type
        • 10.3.2.2.3. By Application
        • 10.3.2.2.4. By Industry
    • 10.3.3. Argentina Data Science Platform Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Deployment
        • 10.3.3.2.2. By Enterprise Type
        • 10.3.3.2.3. By Application
        • 10.3.3.2.4. By Industry

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenges

12. Market Trends & Developments

  • 12.1. Merger & Acquisition (If Any)
  • 12.2. Product Launches (If Any)
  • 12.3. Recent Developments

13. Global Data Science Platform Market: SWOT Analysis

14. Porter's Five Forces Analysis

  • 14.1. Competition in the Industry
  • 14.2. Potential of New Entrants
  • 14.3. Power of Suppliers
  • 14.4. Power of Customers
  • 14.5. Threat of Substitute Products

15. Competitive Landscape

  • 15.1. IBM Corporation
    • 15.1.1. Business Overview
    • 15.1.2. Products & Services
    • 15.1.3. Recent Developments
    • 15.1.4. Key Personnel
    • 15.1.5. SWOT Analysis
  • 15.2. Google LLC
  • 15.3. Microsoft Corporation
  • 15.4. SAS Institute Inc.
  • 15.5. Alteryx Inc.
  • 15.6. Oracle Corporation
  • 15.7. SAP SE
  • 15.8. RapidMiner Inc.
  • 15.9. Dataiku Inc.
  • 15.10. Databricks Inc.

16. Strategic Recommendations

17. About Us & Disclaimer