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市場調查報告書
商品編碼
1733198

全球自主資料平台市場規模：依組件、產業垂直、區域範圍和預測

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個工作天內

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簡介目錄

自主資料平台市場規模與預測

預計2024年自主資料平台市場規模將達到 19.5億美元，到2032年將達到 96.3億美元，在2026-2032年預測期內的年複合成長率為 22.10%。

技術進步將導致雲端基礎的解決方案的採用率不斷提高，認知運算技術和高級分析技術的採用率不斷提高，預計將在未來幾年推動自主資料平台市場的發展。本報告對全球自主資料平台市場進行了全面的評估。它對關鍵細分市場、趨勢、市場促進因素、競爭格局以及在市場中發揮關鍵作用的因素進行了全面的分析。

定義全球自主資料平台市場

自主資料工具分析特定客戶的巨量資料基礎設施，以解決關鍵企業問題並確保最佳資料庫使用率。它是一個資料分析平台，利用人工智慧（AI）和機器學習（ML）等各種認知運算平台來管理和最佳化自身。

透過結合啟發式方法和機器學習，它為使用者提供見解、可操作的警報和建議，提高效能、提高工作負載連續性並節省成本。提高營運效率並簡化流程。根據組件，市場細分為支援和維護、服務、平台、整合和諮詢。根據行業，市場分為通訊和媒體、零售、製造、醫療保健和生命科學等。

全球自主資料平台市場概況

由於技術進步，雲端基礎的解決方案的採用率增加，認知運算技術和進階分析的採用率不斷提高，預計將在預測幾年內推動自主資料平台市場的發展。此外，由於社群媒體和連網設備的使用不斷增加，非結構化資料量的成長預計將在未來幾年推動市場的發展。

此外，可擴展、非結構化和複雜資料的引進以及零售商對全通路體驗的不斷成長的需求預計將在預測期內推動市場發展。也有一些因素和挑戰阻礙了市場成長。熟練專業人員的短缺和複雜的分析過程等因素可能會成為市場發展的限制因素。

目錄

第1章 自主資料平台的全球市場採用情況

  • 市場概覽
  • 研究範圍
  • 先決條件

第2章 執行摘要

第3章 已驗證的市場研究調查方法

  • 資料探勘
  • 驗證
  • 第一手資料
  • 資料來源列表

第4章 自主資料平台的全球市場展望

  • 概述
  • 市場動態
    • 促進因素
    • 限制因素
    • 機會
  • 波特五力模型
  • 價值鏈分析

第5章 全球自主資料平台市場（依組件）

  • 概述
  • 支援和維護
  • 服務
  • 平台
  • 一體化
  • 諮詢

第6章 全球自主資料平台市場（依產業）

  • 概述
  • 通訊媒體
  • 零售
  • BFSI
  • 製造業
  • 醫療保健和生命科學
  • 其他

第7章 全球自主資料平台市場（依地區）

  • 概述
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 其他亞太地區
  • 其他
    • 拉丁美洲
    • 中東和非洲

第8章 全球自主資料平台市場的競爭格局

  • 概述
  • 各公司市場排名
  • 重點發展策略

第9章 公司簡介

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

第10章 重大進展

  • 產品發布/開發
  • 合併與收購
  • 業務擴展
  • 夥伴關係與合作

第11章 附錄

  • 相關調查
簡介目錄
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