查看购物车
  • Traditional Chinese
  • English
  • Japanese
封面
市场调查报告书
商品编码
1641936

资料科学平台：市场占有率分析、产业趋势与统计、成长预测（2025-2030 年）

Data Science Platform - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 207 Pages | 商品交期: 2-3个工作天内

价格

※ 本网页内容可能与最新版本有所差异。详细情况请与我们联繫。

简介目录

资料科学平台市场规模预计在 2025 年为 125.4 亿美元，预计到 2030 年将达到 360.1 亿美元，预测期内（2025-2030 年）的复合年增长率为 23.5%。

数据科学平台-市场-IMG1

资料科学的出现为组织提供了解决方案，将资料集转化为有价值的资源，并透过可操作的见解来增加商业价值。随着公司和组织数量的指数级增长，资料科学正在成为商业各个方面不可或缺的一部分，并在经营模式中发挥至关重要的作用。

主要亮点

  • 资料科学平台提供了一套工具和服务，使组织能够管理、存取和分析资料，简化资料分析流程并扩展他们的资料分析能力。资料科学平台的采用正在成长，因为它具有从预测分析到自动化机器学习过程、做出更明智的决策和更好地利用资料等诸多好处。
  • 公司越来越重视增加内部资料科学资源，以建立机器学习模型并填补稀缺专业人员的招募缺口，从而推动资料科学即服务 (DSaaS) 的兴起。对于许多企业来说，DSaaS 已变得至关重要，因为它可以帮助他们扩展分析能力以满足关键需求并推动期望的业务成果。
  • 随着人工智慧（AI）和机器学习（ML）等技术的快速发展，企业正在接收越来越多的资料，既有资料的，也有基于现有资料集的，而且格式也完全不同。情况就是这样。因此，为了利用这些资料，公司正在转向根据其需求量身定制的资料科学解决方案。
  • 缺乏熟练人才造成的一个主要障碍是无法从组织产生的大量资料中获得有意义的见解。资料科学平台旨在帮助使用者分析和解释复杂的资料，但缺乏熟练的专家来培训这些平台会降低其有效性。组织努力弥合其资料科学平台的先进功能与最佳利用这些功能所需的专业知识之间的差距。
  • 新冠疫情加速了商业和工业的数位化，导致对资料主导洞察的需求激增。各行业的组织现在都在使用资料科学来就资源、风险管理和客户行为做出明智的决策。此外，远距工作的转变刺激了云端基础的资料科学平台和工具的采用，使资料科学家无论身在何处都能有效协作。这种灵活性和可访问性对资料科学知识的需求更大。

资料科学平台市场趋势

小型企业强劲成长

  • 小型企业拥有少于 100 名员工，而中型企业则拥有 100 至 999 名员工。资料科学资料科学在小型企业中的主要用途之一是利用它来追踪销售週期各个阶段的客户。小型企业可以使用资料分析来识别可能进行购买的特定消费者群体。资料主导的产业成长是透过基于证据的结论来提高销售、绩效、业务等，从而实现企业永续发展。
  • 小型企业通常资源有限，因此每个决策都很重要。资料科学平台帮助中小型企业做出更明智的决策并降低风险。该平台帮助中小企业识别效率低下的问题并降低业务和供应链中的成本。
  • 2023 年 8 月，Infor Nexus 与星展银行合作宣布为 Infor Nexus 供应链生态系统中的中小型企业 (SME) 供应商推出出货前融资。该解决方案利用 Infor Nexus 平台的历史资料来提供基于资料库的融资解决方案，以帮助满足供应商营运资金需求。
  • 云端运算的采用预计将推动市场成长。它彻底改变了中小型企业存取和利用资料科学平台的方式。云端基础架构提供可扩展性，允许中小型企业根据其需求的变化无缝地增加或减少资料科学能力。 2023 年 11 月，AnniQ 推出了一项专注于资料分析的新服务，以支援中小企业的策略能力。该服务旨在增强中小企业在业务运营中与资料互动和利用数据的方式，重点提供可操作的见解并推动策略执行。

北美占据主要市场占有率

  • 在资料量和复杂性不断增长的推动下，美国不断创新其资料科学平台并加强其在全球市场的地位。进阶分析、人工智慧（AI）和机器学习（ML）等先进技术的市场引进也对国家经济产生直接影响。
  • 根据电信咨询服务公司估计，美国的网路流量将从 2021 年的每月 6,400 万Exabyte迅速增长到 2023 年的 9,864 万Exabyte。资料流量的大幅增加需要更先进的资料科学解决方案来管理大量资料并根据提取的资料改进解决方案。此外，组织产生的资料比以往任何时候都多，而且这些资料变得越来越复杂和多样化。这使得使用传统方法来分析资料并从中提取见解变得困难。资料科学平台提供管理和分析大型复杂资料库的工具和基础设施。
  • 此外，受访的市场上所有领先供应商都位于美国。此外，该国正处于第四次工业革命的边缘，资料正在大规模生产中得到利用，同时整合整个供应链中不同的製造系统和资料。这导致该国加速采用先进技术。
  • 该地区的政府也采取倡议支持市场上最新技术的发展，促进机器人技术的采用。例如，美国联邦政府推出了国家机器人计画（NRI）项目，以加强国内机器人製造能力并鼓励该领域的研究活动。预计这些倡议将为市场成长带来积极的前景。
  • 此外，加拿大注重医疗保健、人工智慧和可再生能源等领域的研究和创新，也需要资料科学平台来分析复杂的资料集并获得研究见解。加拿大的高科技产业正在蓬勃发展，该国热衷于吸引技术知识。越来越多的行业需要熟练的资料科学家和人工智慧专业人员，包括银行、医疗保健、金融、保险、媒体和娱乐、电信和电子商务。目前对专家的需求大于现有专家的数量。不断增强的技术力和对高阶 IT 解决方案、AI 和 ML 的需求将推动加拿大资料科学平台市场的发展。

资料科学平台产业概况

资料科学平台市场处于半静态状态，产品差异化程度高，产品采用水准不断提高，技术进步迅速，难以维持竞争优势，迫使企业持续采用和创新解决方案。主要参与者包括 Alteryx、IBM Corporation、Google LLC（Alphabet Inc.）、SAS、Alteryx 和 Microsoft Corporation。

  • 2023 年 11 月 - IBM 与 Amazon Web Services (AWS) 合作，全面推出适用于 Db2 的 Amazon Relational Database Service (Amazon RDS)。 Amazon RDS 是一种完全託管的云端服务，旨在让资料库客户更轻鬆地管理混合云端环境中的人工智慧 (AI) 工作负载的资料。这使客户能够利用该公司在 AWS 上的整合资料和 AI 功能来管理他们的资料并扩展他们的 AI 工作负载。
  • 2023 年 8 月 - Google Cloud 和 NVIDIA 扩大伙伴关係，以推进 AI 运算、软体和服务，帮助客户建置和部署用于生成式 AI 的大规模模型并加速资料科学工作负载。此次伙伴关係将为全球一些最大的人工智慧客户提供端到端的机器学习服务，包括让人工智慧超级电脑在基于 NVIDIA 技术建构的 Google Cloud 产品上轻鬆运作。

其他福利：

  • Excel 格式的市场预测 (ME) 表
  • 3 个月的分析师支持

目录

第 1 章 简介

  • 研究假设和市场定义
  • 研究范围

第二章调查方法

第三章执行摘要

第四章 市场洞察

  • 市场概况
  • 产业吸引力－波特五力分析
    • 供应商的议价能力
    • 消费者议价能力
    • 新进入者的威胁
    • 替代品的威胁
    • 竞争对手之间的竞争
  • 宏观经济趋势的影响

第五章 市场动态

  • 市场驱动因素
    • 巨量资料的爆炸性成长
    • 资料科学和机器学习的有前景的使用案例的出现
    • 组织向资料密集方法和决策的转变
  • 市场限制
    • 劳动力缺乏技能
    • 资料安全和信任问题
  • 关键使用案例
  • 生态系分析
  • 定价及定价模式分析
  • 资料科学平台的主要功能（人工智慧和机器学习、分析、视觉化、探索、建模）

第六章 市场细分

  • 透过奉献
    • 平台
    • 服务
  • 按部署
    • 本地
    • 云
  • 按公司规模
    • 中小企业
    • 大型企业
  • 按行业
    • 资讯科技/通讯
    • BFSI
    • 零售与电子商务
    • 石油、天然气和能源
    • 製造业
    • 政府和国防
    • 其他行业
  • 按地区
    • 北美洲
      • 美国
      • 加拿大
    • 欧洲
      • 英国
      • 德国
      • 法国
      • 义大利
      • 西班牙
      • 希腊
      • 其他欧洲国家
    • 亚太地区
      • 中国
      • 印度
      • 日本
      • 澳洲
      • 东南亚
      • 印尼
      • 菲律宾
      • 马来西亚
      • 新加坡
      • 东南亚其他地区
      • 其他亚太地区
    • 拉丁美洲
      • 巴西
      • 阿根廷
      • 墨西哥
      • 其他拉丁美洲国家
    • 中东和非洲
      • 沙乌地阿拉伯
      • GCC
      • 阿拉伯聯合大公国
      • 其他 GCC
      • 南非
      • 其他中东和非洲地区

第七章 竞争格局

  • 公司简介
    • IBM Corporation
    • Google LLC（Alphabet Inc.）
    • Microsoft Corporation
    • SAS
    • Alteryx
    • The MathWorks Inc.
    • RapidMiner
    • Databricks
    • Amazon Web Services Inc.（AMAZON.COM INC.）
    • DataRobot Inc.

第 8 章厂商市场占有率分析

第 9 章区域供应商排名

第十章 投资分析

第 11 章：投资分析市场的未来

简介目录
Product Code: 62382

The Data Science Platform Market size is estimated at USD 12.54 billion in 2025, and is expected to reach USD 36.01 billion by 2030, at a CAGR of 23.5% during the forecast period (2025-2030).

Data Science Platform - Market - IMG1

Data Science is emerging to provide solutions to organizations to transform data sets into a valuable resource that helps get business value with actionable insights. As the number of business enterprises and organizations grows exponentially, data science is becoming essential in various aspects of business and plays a pivotal role in business models.

Key Highlights

  • The data science platforms offer a suite of tools and services that allow organizations to manage, access, and analyze their data and enable organizations to streamline their data analysis processes and scale their data analysis capabilities. The adoption of data science platforms is growing due to benefits such as predictive analytics to automated machine learning processes, informed decisions, and better utilization of their data.
  • There is an increasing emphasis on businesses boosting their internal data science resources to build machine learning models and fill the hiring gap of in-demand professionals, resulting in increased adoption of data science as a service (DSaaS). For many businesses, it becomes essential as it helps them scale their analytics capabilities to meet critical needs and get the desired outcomes of business.
  • As technologies such as artificial intelligence (AI) and machine learning (ML) are advancing rapidly, businesses are receiving a significantly larger amount of data, including new data based on previously existing datasets and new forms of data altogether. Thus, to use these data, businesses are moving to adopt data science solutions that are compatible with their requirements.
  • One of the primary obstacles arising from the lack of a skilled workforce is the inability to derive meaningful insights from the vast volumes of data organizations generate. Data science platforms are designed to allow users to analyze and interpret complex datasets, but the shortage of skilled professionals capable of guiding these platforms diminishes their effectiveness. Organizations struggle to bridge the gap between the advanced functionalities of data science platforms and the expertise needed to leverage these functionalities optimally.
  • The COVID-19 pandemic accelerated the digitization of businesses and industries, leading to a surge in the need for data-driven insights. Organizations across sectors turned to data science to make informed decisions about resource and risk management and customer behavior. Further, the shift to remote work spurred the adoption of cloud-based data science platforms and tools, enabling data scientists to collaborate effectively from any location. This flexibility and accessibility further fueled the demand for data science expertise.

Data Science Platform Market Trends

Small and Medium Enterprises to Witness Major Growth

  • Small-sized organizations have less than 100 employees, whereas medium-sized enterprises have between 100 to 999 employees. One of the major applications of data science for small businesses is using it to track clients throughout the various stages of the sales cycle. Small businesses can utilize data analytics to determine a particular segment of consumers willing to buy. Data-driven industry growth is making evidence-based conclusions to enhance sales, performance, and operations, among others, through which businesses can achieve sustainable development.
  • SMEs often operate with limited resources, making every decision critical. Data science platforms empower SMEs to make more precise and informed decisions, reducing risks. The platforms help SMEs identify inefficiencies in their operations and supply chains, reducing costs.
  • In August 2023, Infor Nexus and DBS Bank, in partnership, announced the launch of pre-shipment financing for small and medium-sized enterprises (SME) suppliers in the Infor Nexus supply chain ecosystem. This solution utilizes historical data from the Infor Nexus platform to provide data-based lending solutions that help suppliers meet their working capital requirements.
  • Cloud adoption is expected to boost the market's growth. It has revolutionized how SMEs access and utilize data science platforms. Cloud infrastructure offers scalability, allowing SMEs to seamlessly scale their data science capabilities up or down based on their changing needs. In November 2023, AnniQ launched a new service focusing on data analytics to support the strategic capabilities of small and medium-sized enterprises (SMEs). This service is designed to enhance how SMEs engage with and utilize data in their business operations, emphasizing providing actionable insights and facilitating strategic execution.

North America to Hold Significant Market Share

  • Fueled by data's increasing volume and complexity, the United States continues to innovate and consolidate its position in the global market in the data science platforms. The embracing of advanced technologies such as advanced analytics, Artificial Intelligence (AI), and Machine Learning (ML) in the market studied has also directly impacted the national economy.
  • According to Telecom Advisory Services, the estimated Internet traffic in the United States has jumped from 64 million exabytes per month in 2021 to 98.64 million exabytes per month in 2023. Such a significant increase in data traffic needs more advanced data science solutions to manage a large amount of data and improve the solutions based on extracted data. Additionally, organizations are generating more data than ever, which is becoming increasingly complex and diverse. This makes it difficult to analyze and extract insights from data using traditional methods. Data science platforms provide the tools and infrastructure to manage and analyze large and complex databases.
  • Moreover, all the major vendors studied in the market are US-based. Additionally, the country is on the brink of the fourth industrial revolution, where data is being utilized in large-scale production while integrating the data with a wide variety of manufacturing systems throughout the supply chain. This is accelerating the adoption of advanced technologies in the country.
  • The government in the region is also promoting the adoption of robotics by taking initiatives to support the growth of modern technologies in the market. For instance, the US federal government has launched the National Robotics Initiative (NRI) program to strengthen the capabilities of building domestic robots in the nation and encourage research activities in the field. Such initiatives are further expected to create a positive outlook for the market growth.
  • In addition, the strong focus on research and innovation in Canada in sectors like healthcare, artificial intelligence, and renewable energy supports the market requiring data science platforms to analyze complex data sets and gain research insights. Canada's tech industry is flourishing, and the country has made a concerted effort to attract technological know-how. A rising number of sectors, including banking, healthcare, finance, insurance, media and entertainment, telecom, and e-commerce, need qualified Data Scientists and AI experts. Professionals are in greater demand right now than they are available. Expanding technological capabilities and the demand for high-end IT solutions, AI, and ML will drive the market for data science platforms in Canada.

Data Science Platform Industry Overview

The Data Science Platform Market is semi-consolidated and is characterized by high product differentiation, growing levels of product penetration, and rapid advancements in technology, leading to difficulty in maintaining a competitive advantage, forcing them to continuously adopt and innovate solutions. Some of major players include Alteryx, IBM Corporation, Google LLC (Alphabet Inc.), SAS, Alteryx, Microsoft Corporation.

  • November 2023 - IBM collaborated with Amazon Web Services (AWS) on the general availability of Amazon Relational Database Service (Amazon RDS) for Db2, a fully managed cloud offering designed to make it easier for database customers to manage data for artificial intelligence (AI) workloads across hybrid cloud environments. It will allow the users to leverage an array of the company's integrated data and AI capabilities on AWS to manage data and scale AI workloads.
  • August 2023 - Google Cloud and NVIDIA announced a partnership expansion to advance AI computing, software, and services for customers to build and deploy massive models for generative AI and speed data science workloads. The partnership will bring end-to-end machine learning services to some of the largest AI customers in the world - including by making it easy to run AI supercomputers with Google Cloud offerings built on NVIDIA technologies.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Bargaining Power of Suppliers
    • 4.2.2 Bargaining Power of Consumers
    • 4.2.3 Threat of New Entrants
    • 4.2.4 Threat of Substitutes
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Impact of Macroeconomic Trends

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Rapid Increase in Big Data
    • 5.1.2 Emerging Promising Use Cases of Data Science and Machine Learning
    • 5.1.3 Shift of Organizations Toward Data-intensive Approach and Decisions
  • 5.2 Market Restraints
    • 5.2.1 Lack of Skillset in Workforce
    • 5.2.2 Data Security and Reliability Concerns
  • 5.3 Key Use Cases
  • 5.4 Ecosystem Analysis
  • 5.5 Analysis of Pricing and Pricing Models
  • 5.6 Key Capabilities of Data Science Platforms (AI & Ml, Analytics, Visualization, Exploration, Modelling)

6 MARKET SEGMENTATION

  • 6.1 By Offering
    • 6.1.1 Platform
    • 6.1.2 Services
  • 6.2 By Deployment
    • 6.2.1 On-premise
    • 6.2.2 Cloud
  • 6.3 By Size of Enterprises
    • 6.3.1 Small and Medium Enterprises
    • 6.3.2 Large Enterprises
  • 6.4 By Industry Vertical
    • 6.4.1 IT and Telecom
    • 6.4.2 BFSI
    • 6.4.3 Retail and E-commerce
    • 6.4.4 Oil Gas and Energy
    • 6.4.5 Manufacturing
    • 6.4.6 Government and Defense
    • 6.4.7 Other Industry Verticals
  • 6.5 By Geography
    • 6.5.1 North America
      • 6.5.1.1 United States
      • 6.5.1.2 Canada
    • 6.5.2 Europe
      • 6.5.2.1 United Kingdom
      • 6.5.2.2 Germany
      • 6.5.2.3 France
      • 6.5.2.4 Italy
      • 6.5.2.5 Spain
      • 6.5.2.6 Greece
      • 6.5.2.7 Rest of Europe
    • 6.5.3 Asia Pacific
      • 6.5.3.1 China
      • 6.5.3.2 India
      • 6.5.3.3 Japan
      • 6.5.3.4 Australia
      • 6.5.3.5 Southeast Asia
      • 6.5.3.5.1 Indonesia
      • 6.5.3.5.2 Philippines
      • 6.5.3.5.3 Malaysia
      • 6.5.3.5.4 Singapore
      • 6.5.3.5.5 Rest of Southeast Asia
      • 6.5.3.6 Rest of Asia Pacific
    • 6.5.4 Latin America
      • 6.5.4.1 Brazil
      • 6.5.4.2 Argentina
      • 6.5.4.3 Mexico
      • 6.5.4.4 Rest of Latin America
    • 6.5.5 Middle East and Africa
      • 6.5.5.1 Saudi Arabia
      • 6.5.5.2 GCC
      • 6.5.5.2.1 United Arab Emirates
      • 6.5.5.2.2 Rest of GCC
      • 6.5.5.3 South Africa
      • 6.5.5.4 Rest of Middle East and Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 IBM Corporation
    • 7.1.2 Google LLC (Alphabet Inc.)
    • 7.1.3 Microsoft Corporation
    • 7.1.4 SAS
    • 7.1.5 Alteryx
    • 7.1.6 The MathWorks Inc.
    • 7.1.7 RapidMiner
    • 7.1.8 Databricks
    • 7.1.9 Amazon Web Services Inc. (AMAZON.COM INC.)
    • 7.1.10 DataRobot Inc.

8 VENDOR SHARE ANALYSIS

9 RANKING OF VENDORS AT A REGIONAL LEVEL

10 INVESTMENT ANALYSIS

11 FUTURE OF THE MARKET