资料科学平台市场 - 全球产业规模、份额、趋势、机会及预测（按部署方式、公司类型、应用、产业、地区和竞争格局划分），2021-2031年

全球资料科学平台市场预计将从 2025 年的 585.3 亿美元成长到 2031 年的 2,255.3 亿美元，复合年增长率达到 25.21%。

这些平台作为整合软体基础设施，支援从资料准备和模型训练到最终部署和持续监控的整个分析生命週期。关键成长要素包括人工智慧营运需求的日益增长，以及对能够优化工程团队和相关人员之间工作流程的协作生态系统的需求。此外，在管理大规模资料集时对集中控制和可重现性的需求，也持续推动该领域在国际工业界的稳定成长。

儘管市场规模不断扩大，但能够有效运作这些复杂生态系统的专业人才严重短缺，阻碍了市场发展。企业在招募具备统计和技术技能的人才方面常常面临挑战，而这些技能对于有效利用这些工具至关重要，从而导致实施瓶颈。根据电脑产业协会 (CompTIA) 的数据，预计到 2024 年，数据科学家和分析师的就业成长率将达到 5.5%，但这一需求远远超过了合格人才的供应。这种日益扩大的技能缺口增加了实施策略的复杂性，并延缓了企业实现投资回报的时间。

市场驱动因素

人工智慧 (AI) 和机器学习技术的快速普及推动了对强大营运基础设施的需求，并将资料科学平台确立为企业不可或缺的资源。随着企业从实验阶段过渡到全面应用，它们在模型管治、可扩展性和生命週期管理方面面临着许多复杂挑战——而整合平台正是为应对这些挑战而专门建构的。根据 IBM 统计，截至 2024 年 1 月，约 42% 的企业级组织已将 AI 积极整合到其营运中，这对支援如此广泛应用的系统提出了巨大的要求。因此，平台正在不断发展，以优化从开发到生产的流程，并确保分析投资能带来可衡量的成果。 Databricks 发布的《2024 年数据与 AI 现况报告》也印证了这一点，该报告指出，与前一年相比，已部署到生产环境中的 AI 模型数量增长了 11 倍。

同时，资料科学正日益普及，市场进入不再局限于专业工程团队，公民资料科学科学家也逐渐成为主流。为了平衡技术复杂性与业务效用，供应商正在加速采用低程式码/无程式码介面，使非技术相关人员能够直接参与分析工作流程。这种转变最大限度地减少了瓶颈，并在整个组织内培育以数据为中心的文化。根据Google云端于2024年3月发布的《2024年数据与人工智慧趋势报告》，约三分之二的数据决策者希望全年都能更便捷地获取洞察，这主要得益于生成式人工智慧能力的提升。透过向更广泛的员工群体提供先进的分析工具，资料科学平台能够帮助企业扩展决策能力，并优化数据投资盈利。

市场挑战

熟练的专业人才严重短缺是限制全球资料科学平台市场成长的主要障碍。随着企业采用更先进的软体基础设施来运作人工智慧和机器学习，它们日益面临能够管理这些复杂生态系统的人才短缺问题。由于缺乏必要的人力资本来监督技术工作流程，企业难以将原始数据转化为可执行的洞察，造成严重的实施瓶颈。因此，企业面临计划延期和实施计画停滞不前的问题，直接导致预期投资收益的实现延迟。

近期来自供给面的统计数据凸显了日益扩大的技能缺口的严重性：根据美国统计协会的数据，到2024年，资料科学硕士课程每年将培养约2400名毕业生，这一数字远远不足以满足行业快速增长的需求。合格人才的短缺导致企业间对数量有限的专业人才展开激烈竞争，造成营运摩擦，阻碍了资料科学平台的广泛应用和高效利用。

市场趋势

随着企业面临日益增长的监管压力以及黑箱演算法带来的风险，符合伦理的人工智慧管治和可解释性框架的重要性日益凸显。随着资料科学从实验计划走向核心业务运营，平台越来越需要整合严格的监督机制，以确保演算法决策的透明度、公平性和课责。这一趋势源自于弥合快速技术应用与组织管理相关风险能力之间差距的迫切需求。思科于2024年12月发布的《2024年人工智慧就绪指数》显示，仅有31%的组织表示已全面落实人工智慧政策，凸显了市场对能够提供整合管治解决方案、满足复杂合规要求的平台的迫切需求。

同时，生成式人工智慧与合成资料能力的融合正在变革平台架构，从而促进高阶人工智慧应用的创建。供应商正迅速采用向量搜寻和搜寻增强生成（RAG）管道，将其平台发展成为建立和管理大规模语言模型（LLM）工作流程的强大引擎。这项技术进步使资料团队能够基于自身企业资料建立生成模型，在确保安全性的同时提高准确性和相关性。这种变革的规模体现在采用率数据中。根据Databricks于2024年3月发布的《2024年数据与人工智慧现况报告》，该公司生态系统内的向量资料库使用量年增377%，凸显了基础设施向支援高阶生成式人工智慧开发的重大转变。

目录

第一章概述

第二章调查方法

第三章执行摘要

第四章：客户评价

第五章 全球资料科学平台市场展望

• 市场规模及预测
  • 按金额
• 市占率及预测
  • 按部署类型（云端/本地部署）
  • 按公司规模（大型公司、中小企业）
  • 按应用领域划分（客户支援、业务营运、行销、财务/会计、物流等）
  • 按行业划分（银行、金融、保险、IT、通讯、医疗保健、零售、製造、运输、其他）
  • 按地区
  • 按公司（2025 年）
• 市场地图

第六章：北美资料科学平台市场展望

• 市场规模及预测
• 市占率及预测
• 北美洲：国家分析
  • 我们
  • 加拿大
  • 墨西哥

7. 欧洲资料科学平台市场展望

• 市场规模及预测
• 市占率及预测
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙

8. 亚太地区资料科学平台市场展望

• 市场规模及预测
• 市占率及预测
• 亚太地区：国家分析
  • 中国
  • 印度
  • 日本
  • 韩国
  • 澳洲

9. 中东和非洲资料科学平台市场展望

• 市场规模及预测
• 市占率及预测
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 南非

10. 南美洲资料科学平台市场展望

• 市场规模及预测
• 市占率及预测
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷

第十一章 市场动态

• 司机
• 任务

第十二章 市场趋势与发展

• 併购
• 产品发布
• 最新进展

第十三章 全球资料科学平台市场：SWOT分析

第十四章：波特五力分析

• 产业竞争
• 新进入者的可能性
• 供应商电力
• 顾客权力
• 替代品的威胁

第十五章 竞争格局

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

第十六章 策略建议

第十七章：关于研究公司及免责声明

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.

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