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封面
市场调查报告书
商品编码
1733198

全球自主资料平台市场规模（按组件、产业垂直、区域范围和预测）

Global Autonomous Data Platform Market Size By Component (Services, Platform, Integration), By Vertical (Retail, BFSI, Manufacturing), By Geographic Scope And Forecast
出版日期: | 出版商: Verified Market Research | 英文 | 商品交期: 2-3个工作天内

价格
简介目录

自主数据平台市场规模与预测

预计 2024 年自主资料平台市场规模将达到 19.5 亿美元，到 2032 年将达到 96.3 亿美元，在 2026-2032 年预测期内的复合年增长率为 22.10%。

技术进步将导致云端基础的解决方案的采用率不断提高，认知运算技术和高级分析技术的采用率不断提高，预计将在未来几年推动自主数据平台市场的发展。本报告对全球自主数据平台市场进行了全面的评估。它对关键细分市场、趋势、市场驱动因素、竞争格局以及在市场中发挥关键作用的因素进行了全面的分析。

定义全球自主数据平台市场

自主资料工具分析特定客户的巨量资料基础设施，以解决关键企业问题并确保最佳资料库使用率。它是一个数据分析平台，利用人工智慧（AI）和机器学习（ML）等各种认知运算平台来管理和优化自身。

透过结合启发式方法和机器学习，它为使用者提供见解、可操作的警报和建议，从而提高效能、提高工作负载连续性并节省成本。提高营运效率并简化流程。根据组件，市场细分为支援和维护、服务、平台、整合和咨询。根据行业，市场分为通讯和媒体、零售、製造、医疗保健和生命科学等。

全球自主数据平台市场概况

由于技术进步，云端基础的解决方案的采用率增加，认知运算技术和进阶分析的采用率不断提高，预计将在预测几年内推动自主资料平台市场的发展。此外，由于社群媒体和连网设备的使用不断增加，非结构化资料量的成长预计将在未来几年推动市场的发展。

此外，可扩展、非结构化和复杂数据的引入以及零售商对全通路体验的不断增长的需求预计将在预测期内推动市场发展。也有一些因素和挑战阻碍了市场成长。熟练专业人员的短缺和复杂的分析过程等因素可能会成为市场发展的限制因素。

目录

第一章 自主资料平台的全球市场采用情况

  • 市场概览
  • 研究范围
  • 先决条件

第二章执行摘要

第三章：已验证的市场研究调查方法

  • 资料探勘
  • 验证
  • 第一手资料
  • 资料来源列表

第四章：自主资料平台的全球市场展望

  • 概述
  • 市场动态
    • 驱动程式
    • 限制因素
    • 机会
  • 波特五力模型
  • 价值链分析

5. 全球自主资料平台市场（按组件）

  • 概述
  • 支援和维护
  • 服务
  • 平台
  • 一体化
  • 咨询

6. 全球自主资料平台市场（依产业垂直划分）

  • 概述
  • 通讯媒体
  • 零售
  • BFSI
  • 製造业
  • 医疗保健和生命科学
  • 其他的

7. 全球自主资料平台市场（按地区）

  • 概述
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 其他亚太地区
  • 其他的
    • 拉丁美洲
    • 中东和非洲

8. 全球自主数据平台市场的竞争格局

  • 概述
  • 各公司市场排名
  • 重点发展策略

第九章 公司简介

  • Oracle Corporation
  • Teradata Corporation
  • IBM Corporation
  • Amazon Web Services, Inc.
  • MapR
  • Cloudera, Inc.
  • Qubole, Inc.
  • Ataccama Corporation
  • Gemini Data, Inc.
  • DvSum

第十章 重大进展

  • 产品发布/开发
  • 合併与收购
  • 业务扩展
  • 伙伴关係与合作

第十一章 附录

  • 相关调查
简介目录
Product Code: 33211

Autonomous Data Platform Market Size And Forecast

Autonomous Data Platform Market size was valued at USD 1.95 Billion in 2024 and is projected to reach USD 9.63 Billion by 2032, growing at a CAGR of 22.10 % from 2026 to 2032.

The increasing adoption of cloud-based solutions owing to technological advancement and the rising adoption of cognitive computing technology & advanced analytics are expected to drive the Autonomous Data Platform Market over the predicted years. The Global Autonomous Data Platform Market report provides a holistic evaluation of the market. The report offers a comprehensive analysis of key segments, trends, drivers, restraints, competitive landscape, and factors that are playing a substantial role in the market.

Global Autonomous Data Platform Market Definition

The autonomous data tool analyses a specific customer's big data infrastructure to address crucial company problems and ensure optimal database usage. It is a data and analytics platform that manages and optimizes itself by leveraging various cognitive computing platforms, such as Artificial Intelligence (AI), and Machine Learning (ML).

By making use of the combination of heuristics and machine learning, it serves insights, actionable alerts, and recommendations to the users, which results in ensuring high performance, workload continuity, and cost savings. It enhances operational efficiency and makes the process easier. Based on the component, the market is classified into Support & Maintenance, Services, Platform, Integration, and Advisory. Based on the vertical, the market is bifurcated into Telecommunication & Media, Retail, Manufacturing, Healthcare & Life Sciences, and Others.

Global Autonomous Data Platform Market Overview

The increasing adoption of cloud-based solutions owing to the advancement in technologies and the rising adoption of cognitive computing technology & advanced analytics are expected to drive the Autonomous Data Platform Market over the predicted years. Also, the growing volume of unstructured data with respect to the increasing utilization of social media & interconnected devices expects a boost to the market in the coming years.

Moreover, the introduction of expandable, unstructured, & complex data and the increasing demand for omnichannel experience from retailers are anticipated to fuel the market during the forecasted period. There are certain restraints and challenges faced which can hinder market growth. Factors such as the dearth of skilled professionals and complicated analytical processes are likely to act as market restraints.

Global Autonomous Data Platform Market: Segmentation Analysis

The Global Autonomous Data Platform Market is Segmented on the basis of Component, Vertical, And Geography.

Autonomous Data Platform Market, By Component

  • Support and Maintenance
  • Services
  • Platform
  • Integration
  • Advisory

Based on Component, the market is bifurcated into Support & Maintenance, Services, Platform, Integration, and Advisory. A wide range of applications of the Autonomous Data Platform in various industry segments is expected to bolster the market demand in the coming years.

Autonomous Data Platform Market, By Vertical

  • Telecommunication & Media
  • Retail
  • BFSI
  • Manufacturing
  • Healthcare and Life Sciences
  • Others

Based on Vertical, the market is bifurcated into Telecommunication & Media, Retail, BFSI, Manufacturing, Healthcare & Life Sciences, and Others. BFSI segment is predicted to hold the most significant CAGR in the forecasted period due to the rapid adoption of the Autonomous Data Platform in this segment.

Autonomous Data Platform Market, By Geography

  • North America
  • Europe
  • Asia Pacific
  • Rest of the world
  • On the basis of Regional Analysis, the Global Autonomous Data Platform Market is classified into North America, Europe, Asia Pacific, and the Rest of the world. The largest share in the market will be dominated by Europe owing to the rise of inconsistent data generated across organizations in different European countries.

Key Players

The "Global Autonomous Data Platform Market" study report will provide valuable insight with an emphasis on the global market including some of the major players such as Oracle Corporation, Teradata Corporation, IBM Corporation, Amazon Web Services, Inc., MapR, Cloudera, Inc., Qubole, Inc, Ataccama Corporation, Gemini Data, Inc, DvSum.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with its product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Key Developments

  • Partnerships, Collaborations, and Agreements
  • Qubole, Inc. teamed up with Ascend.io, a data engineering firm, in 2020 to combine the world's most powerful data pipeline autonomous technology with the most comprehensive data lake platform. It enables data teams to construct self-service data pipelines 7x faster and with 95% less code, lowering infrastructure costs by 50% or more while improving data processing efficiency.
  • June 2020 - Anaconda, Inc., the leading Python data science platform provider, and IBM Watson have announced a new partnership to enable enterprises to adopt AI open-source technology more easily. By collaborating, the two companies hope to boost innovation and overcome the AI and data science skills gap that many businesses are experiencing. The Anaconda Team Edition repository will be integrated with IBM Watson Studio on IBM Cloud Pak for Data, allowing businesses to better manage and accelerate the adoption of AI open-source technologies across any cloud.
  • Product Launches and Product Expansions
  • Qubole introduced a self-service platform in June 2019 for data scientists and engineers to construct AI, machine learning, and analytics processes on their preferred public cloud.
  • MapR announced new MapR Data Platform innovations in April 2019, including new, deep integrations with Kubernetes key components for primary workloads on Spark and Drill. The platform was able to better manage extremely elastic workloads as a result of this innovation.
  • Oracle launched a cloud-based data science platform in 2020, with Oracle Cloud Infrastructure Data Science at its heart. It allows users to train, manage, and create machine learning algorithms on the Oracle Cloud.

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL AUTONOMOUS DATA PLATFORM MARKET

  • 1.1 Overview of the Market
  • 1.2 Scope of Report
  • 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

  • 3.1 Data Mining
  • 3.2 Validation
  • 3.3 Primary Interviews
  • 3.4 List of Data Sources

4 GLOBAL AUTONOMOUS DATA PLATFORM MARKET OUTLOOK

  • 4.1 Overview
  • 4.2 Market Dynamics
    • 4.2.1 Drivers
    • 4.2.2 Restraints
    • 4.2.3 Opportunities
  • 4.3 Porters Five Force Model
  • 4.4 Value Chain Analysis

5 GLOBAL AUTONOMOUS DATA PLATFORM MARKET, BY COMPONENT

  • 5.1 Overview
  • 5.2 Support and Maintenance
  • 5.3 Services
  • 5.4 Platform
  • 5.5 Integration
  • 5.6 Advisory

6 GLOBAL AUTONOMOUS DATA PLATFORM MARKET, BY VERTICAL

  • 6.1 Overview
  • 6.2 Telecommunication & Media
  • 6.3 Retail
  • 6.4 BFSI
  • 6.5 Manufacturing
  • 6.6 Healthcare and Life Sciences
  • 6.7 Others

7 GLOBAL AUTONOMOUS DATA PLATFORM MARKET, BY GEOGRAPHY

  • 7.1 Overview
  • 7.2 North America
    • 7.2.1 U.S.
    • 7.2.2 Canada
    • 7.2.3 Mexico
  • 7.3 Europe
    • 7.3.1 Germany
    • 7.3.2 U.K.
    • 7.3.3 France
    • 7.3.4 Rest of Europe
  • 7.4 Asia Pacific
    • 7.4.1 China
    • 7.4.2 Japan
    • 7.4.3 India
    • 7.4.4 Rest of Asia Pacific
  • 7.5 Rest of the World
    • 7.5.1 Latin America
    • 7.5.2 Middle East & Africa

8 GLOBAL AUTONOMOUS DATA PLATFORM MARKET COMPETITIVE LANDSCAPE

  • 8.1 Overview
  • 8.2 Company Market Ranking
  • 8.3 Key Development Strategies

9 COMPANY PROFILES

  • 9.1 Oracle Corporation
    • 9.1.1 Overview
    • 9.1.2 Financial Performance
    • 9.1.3 Product Outlook
    • 9.1.4 Key Developments
  • 9.2 Teradata Corporation
    • 9.2.1 Overview
    • 9.2.2 Financial Performance
    • 9.2.3 Product Outlook
    • 9.2.4 Key Developments
  • 9.3 IBM Corporation
    • 9.3.1 Overview
    • 9.3.2 Financial Performance
    • 9.3.3 Product Outlook
    • 9.3.4 Key Developments
  • 9.4 Amazon Web Services, Inc.
    • 9.4.1 Overview
    • 9.4.2 Financial Performance
    • 9.4.3 Product Outlook
    • 9.4.4 Key Developments
  • 9.5 MapR
    • 9.5.1 Overview
    • 9.5.2 Financial Performance
    • 9.5.3 Product Outlook
    • 9.5.4 Key Developments
  • 9.6 Cloudera, Inc.
    • 9.6.1 Overview
    • 9.6.2 Financial Performance
    • 9.6.3 Product Outlook
    • 9.6.4 Key Developments
  • 9.7 Qubole, Inc.
    • 9.7.1 Overview
    • 9.7.2 Financial Performance
    • 9.7.3 Product Outlook
    • 9.7.4 Key Developments
  • 9.8 Ataccama Corporation
    • 9.8.1 Overview
    • 9.8.2 Financial Performance
    • 9.8.3 Product Outlook
    • 9.8.4 Key Developments
  • 9.9 Gemini Data, Inc.
    • 9.9.1 Overview
    • 9.9.2 Financial Performance
    • 9.9.3 Product Outlook
    • 9.9.4 Key Developments
  • 9.10 DvSum
    • 9.10.1 Overview
    • 9.10.2 Financial Performance
    • 9.10.3 Product Outlook
    • 9.10.4 Key Developments

10 KEY DEVELOPMENTS

  • 10.1 Product Launches/Developments
  • 10.2 Mergers and Acquisitions
  • 10.3 Business Expansions
  • 10.4 Partnerships and Collaborations

11 Appendix

  • 11.1 Related Research