查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1821510

巨量资料的全球市场 (～2035年):零组件·用途·产业·传送模式·技术·业务功能·企业规模·各地区

Big Data Market Till 2035: Distribution by Type of Component, Area of Application, Type of Industry Vertical, Delivery Mode, Type of Technology, Type of Business Function, Company Size, and Key Geographical Regions: Industry Trends and Global Forecasts
出版日期: | 出版商: Roots Analysis | 英文 196 Pages | 商品交期: 2-10个工作天内

价格
简介目录

预计到 2035 年，全球大数据市场规模将从目前的 2,628.7 亿美元成长至 1.019 兆美元，预测期内复合年增长率为 13.10%。

Big Data Market-IMG1

巨量资料市场机会:分类

零组件

  • 硬体设备
  • 服务
  • 软体

用途

  • 高度的分析
  • 资料发现和视觉化
  • 其他

产业

  • 航太·防卫
  • BFSI
  • 能源·电力
  • 工程&建设
  • 医疗保健&製药
  • 製造
  • 媒体&娱乐
  • 零售
  • 通讯·IT
  • 运输·物流

传送模式

  • 云端
  • 内部部署

技术

  • 分析
  • 资料库
  • 提供(播送)工具
  • 机器学习和Hadoop
  • 预测分析
  • 视觉化
  • 其他

业务功能

  • 财务
  • 人事
  • 行销·营业
  • 营运

企业规模

  • 大企业
  • 中小企业

地区

  • 北美
  • 美国
  • 加拿大
  • 墨西哥
  • 其他
  • 欧洲
  • 奥地利
  • 比利时
  • 丹麦
  • 法国
  • 德国
  • 爱尔兰
  • 义大利
  • 荷兰
  • 挪威
  • 俄罗斯
  • 西班牙
  • 瑞典
  • 瑞士
  • 英国
  • 其他
  • 亚洲
  • 中国
  • 印度
  • 日本
  • 新加坡
  • 韩国
  • 其他
  • 南美
  • 巴西
  • 智利
  • 哥伦比亚
  • 委内瑞拉
  • 其他
  • 中东·北非
  • 埃及
  • 伊朗
  • 伊拉克
  • 以色列
  • 科威特
  • 沙乌地阿拉伯
  • 阿拉伯联合大公国
  • 其他
  • 全球其他地区
  • 澳洲
  • 纽西兰
  • 其他

大数据市场：成长与趋势

大数据是指随时间不断成长的、极为庞大的结构化、非结构化或半结构化资料集合。由于海量资料集的速度、规模和复杂性，传统资料管理系统难以储存和处理这些海量资料集，因此难以对其进行分析并从中获取洞察。预计未来几年大数据市场将大幅成长，这得益于企业营运各环节对资料收集、储存和利用的日益重视，以及对高阶分析解决方案和即时处理的需求不断增长。

此外，人工智慧对大数据分析和机器学习 (ML) 的影响日益增强，预计将推动大数据与先进工具的整合，从而能够处理高度复杂的数据集并提供卓越的分析洞察。此外，在预测期内，基于云端的大数据解决方案和行业特定分析工具等领域的持续研发工作预计将推动大数据市场的显着成长。

本报告研究了全球大数据市场，并提供了全面的概述、背景、市场影响因素分析、市场规模趋势和预测、按细分市场和地区进行的详细分析、竞争格局以及主要公司的概况。

目录

章节I:报告概要

第1章 序文

第2章 调查手法

第3章 市场动态

第4章 宏观经济指标

章节II:定性洞察

第5章 摘要整理

第6章 简介

第7章 法规Scenario

章节III:市场概要

第8章 主要企业的总括性资料库

第9章 竞争情形

第10章 閒置频段分析

第11章 企业的竞争力分析

第12章 巨量资料市场上Start-Ups生态系统

章节IV:企业简介

第13章 企业简介

  • 章概要
  • Accenture
  • Alteryx
  • Amazon Web Services
  • Cloudera
  • Cisco Systems
  • Dell
  • EMC
  • Equifax
  • Fair Issac
  • Firebolt
  • Google
  • Hitachi
  • IBM
  • Informatica
  • Microsoft
  • Mu Sigma
  • Oracle
  • OPERA

章节V:市场趋势

第14章 大趋势的分析

第15章 未满足需求分析

第16章 专利分析

第17章 最近的趋势

章节VI:市场机会分析

第18章 全球巨量资料市场

第19章 各零件的市场机会

第20章 各用途的市场机会

第21章 各产业的市场机会

第22章 传送模式的市场机会

第23章 各技术的市场机会

第24章 业务各功能的市场机会

第25章 不同企业规模的市场机会

第26章 北美巨量资料的市场机会

第27章 欧洲的巨量资料的市场机会

第28章 亚洲的巨量资料的市场机会

第29章 中东·北非的巨量资料的市场机会

第30章 南美的巨量资料的市场机会

第31章 全球其他地区的巨量资料的市场机会

第32章 市场集中分析:主要企业的分布

第33章 邻近市场的分析

章节VII:策略工具

第34章 胜利策略

第35章 波特的五力分析

第36章 SWOT分析

第37章 价值链分析

第38章 ROOTS的策略建议

章节VIII:其他独家洞察

第39章 初步研究见解

第40章 报告书的结论

章节IX:附录

第41章 表格形式资料

第42章 企业·团体一览

第43章 客制化的机会

第44章 ROOTS订阅服务

第45章 着者详细内容

简介目录
Product Code: RAICT300318

Big Data Market Overview

As per Roots Analysis, the global big data market size is estimated to grow from USD 262.87 billion in the current year to USD 1,019 billion by 2035, at a CAGR of 13.10% during the forecast period, till 2035.

Big Data Market - IMG1

The opportunity for big data market has been distributed across the following segments:

Type of Component

  • Hardware
  • Services
  • Software

Areas of Application

  • Advanced Analytics
  • Data Discovery and Visualization
  • Others

Type of Industry Vertical

  • Aerospace & Defense
  • BFSI
  • Energy & Power
  • Engineering & Construction
  • Healthcare & Pharmaceuticals
  • Manufacturing
  • Media & Entertainment
  • Retail
  • Telecom & IT
  • Transportation & Logistics

Type of Delivery Mode

  • Cloud
  • On-Premises

Type of Technology

  • Analytics
  • Database
  • Distribution Tools
  • Machine Learning and Hadoop
  • Predictive Analytics
  • Visualization
  • Others

Type of Business Function

  • Finance
  • Human Resources
  • Marketing and Sales
  • Operations

Company Size

  • Large Enterprises
  • Small and Medium Enterprises

Geographical Regions

  • North America
  • US
  • Canada
  • Mexico
  • Other North American countries
  • Europe
  • Austria
  • Belgium
  • Denmark
  • France
  • Germany
  • Ireland
  • Italy
  • Netherlands
  • Norway
  • Russia
  • Spain
  • Sweden
  • Switzerland
  • UK
  • Other European countries
  • Asia
  • China
  • India
  • Japan
  • Singapore
  • South Korea
  • Other Asian countries
  • Latin America
  • Brazil
  • Chile
  • Colombia
  • Venezuela
  • Other Latin American countries
  • Middle East and North Africa
  • Egypt
  • Iran
  • Iraq
  • Israel
  • Kuwait
  • Saudi Arabia
  • UAE
  • Other MENA countries
  • Rest of the World
  • Australia
  • New Zealand
  • Other countries

Big Data Market: Growth and Trends

Big data refers to extremely large collections of structured, unstructured, or semi-structured datasets that continually grow over time. Traditional data management systems find it difficult to store or process these enormous datasets due to their intricate velocity, volume, and variety, which presents challenges for conventional systems in analyzing and deriving insights. The growing emphasis on gathering, storing, and leveraging data across various business aspects, combined with the increasing demand for sophisticated analytics solutions and trends like real-time processing, is likely to drive substantial growth in the big data market in the upcoming years.

The escalating influence of AI on big data analytics and machine learning (ML) is expected to aid in the integration of advanced tools with big data, allowing for the processing of highly complex datasets and providing remarkable analytical insights. Further, owing to the ongoing research and development initiatives in areas such as cloud-based big data solutions, industry-specific analytical tools, big data market is expected to grow significantly during the forecast period.

Big Data Market: Key Segments

Market Share by Type of Component

Based on type of component, the global big data market is segmented into hardware, services and software. According to our estimates, currently, the software segment captures the majority of the market share. This growth can be attributed to the diverse range of solutions offered in this segment, such as credit risk management, business intelligence, CRM analytics, compliance analytics, workforce analytics, and others.

As organizations move towards digital platforms, there is a noticeable increase in the adoption of business intelligence solutions, customer relationship management tools, and workforce analytics. These solutions empower organizations by providing real-time insights, predictive capabilities, and data visualization, which ultimately improve their decision-making processes.

Market Share by Areas of Application

Based on areas of application, the global big data market is segmented into advanced analytics, data discovery and visualization, and others. According to our estimates, currently, the advanced analytics segment captures the majority of the market share. It is anticipated that this segment will continue to lead as businesses increasingly depend on advanced analytical methods to understand complex datasets and improve operational efficiency.

However, the data discovery and visualization segment is expected to grow at a relatively higher CAGR during the forecast period. This growth can be attributed to the increasing need for tools that allow users to easily visualize and understand large amounts of data, facilitating faster insights and encouraging a data-driven environment within organizations.

Market Share by Type of Industry Vertical

Based on type of industry vertical, the global big data market is segmented into aerospace & defense, BFSI, energy & power, engineering & construction, healthcare & pharmaceuticals, manufacturing, media & entertainment, retail, telecom & IT, transportation & logistics. According to our estimates, currently, the BFSI segment captures the majority of the market share. This can be attributed to the growing demand for real-time analysis of extensive transactional data to uncover fraud, mitigate risks, and tailor customer services.

However, the healthcare and pharmaceutical sector is expected to grow at a relatively higher CAGR during the forecast period. This growth is primarily attributed to the increasing need for advanced analytics aimed at enhancing patient outcomes, optimizing operations, and adhering to regulatory standards. The adoption of big data technologies in healthcare allows organizations to examine large amounts of patient data for insights that improve care delivery and operational effectiveness.

Market Share by Type of Delivery Mode

Based on type of delivery mode, the global big data market is segmented into cloud and on-premises. According to our estimates, currently, the cloud segment captures the majority of the market share. This growth can be attributed to its improved scalability, flexibility, and cost-effectiveness. Organizations are increasingly leaning towards cloud solutions because they facilitate the rapid deployment of applications without requiring extensive IT infrastructure, allowing businesses to access big data tools quickly and efficiently.

Market Share by Type of Technology

Based on type of technology, the global big data market is segmented into analytics, database, distribution tools, machine learning and Hadoop, predictive analytics, visualization and others. According to our estimates, currently, the analytics segment captures the majority of the market share. This can be attributed to the growing application of data analytics across various sectors, such as healthcare, media and entertainment, transportation, banking, and e-commerce. However, the visualization segment is expected to grow at a relatively higher CAGR during the forecast period. This can be attributed to the ease of access to data that visualization offers, which enhances opportunities for exploration and collaboration, thereby facilitating informed decision-making.

Market Share by Type of Business Function

Based on type of business function, the global big data market is segmented into finance, human resources, marketing and sales, and operations. According to our estimates, currently, the marketing and sales segment captures the majority of the market share. This can be attributed to the growing demand for data-driven marketing analytics solutions aimed at enhancing customer engagement, optimizing campaigns, and boosting sales performance.

However, the human resources segment is expected to grow at a relatively higher CAGR during the forecast period, owing to the broader acceptance of big data analytics in HR functions, including talent acquisition, employee performance management, and workforce planning

Market Share by Company Size

Based on company size, the global big data market is segmented into large and small and medium enterprise. According to our estimates, currently, the large enterprise segment captures the majority of the market share. However, the small and medium enterprises are expected to grow at a relatively higher CAGR during the forecast period. This can be attributed to their agility, innovation, focus on specialized markets, and capacity to adapt to evolving customer preferences and market dynamics.

Market Share by Geographical Regions

Based on geographical regions, the big data market is segmented into North America, Europe, Asia, Latin America, Middle East and North Africa, and the rest of the world. According to our estimates, currently, North America captures the majority share of the market. This can be attributed to the rise in investments by organizations in research and development efforts aimed at enhancing efficiency and streamlining operational procedures is also driving market expansion.

Example Players in Big Data Market

  • Accenture
  • Alteryx
  • Amazon Web Services
  • Cloudera
  • Cisco Systems
  • Dell
  • EMC
  • Equifax
  • Fair Isaac
  • Firebolt
  • Google
  • Hewlett-Packard Company
  • Hitachi
  • IBM
  • Informatica
  • Microsoft
  • Mu Sigma
  • Oracle
  • OPERA
  • QlikTech
  • Salesforce
  • SAS
  • Sisense
  • Splunk
  • Tableau Software
  • Teradata
  • TIBCO Software
  • TransUnion
  • VMware

Big Data Market: Research Coverage

The report on the big data market features insights on various sections, including:

  • Market Sizing and Opportunity Analysis: An in-depth analysis of the big data market, focusing on key market segments, including [A] type of component, [B] area of application, [C] type of industry vertical, [D] type of delivery mode, [E] type of technology, [F] type of business function, [G] company size, and [H] geographical regions.
  • Competitive Landscape: A comprehensive analysis of the companies engaged in the big data market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
  • Company Profiles: Elaborate profiles of prominent players engaged in the big data market, providing details on [A] location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] big data portfolio, [J] moat analysis, [K] recent developments, and an informed future outlook.
  • Megatrends: An evaluation of ongoing megatrends in the big data industry.
  • Patent Analysis: An insightful analysis of patents filed / granted in the big data domain, based on relevant parameters, including [A] type of patent, [B] patent publication year, [C] patent age and [D] leading players.
  • Recent Developments: An overview of the recent developments made in the big data market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
  • Porter's Five Forces Analysis: An analysis of five competitive forces prevailing in the big data market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors
  • SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.
  • Value Chain Analysis: A comprehensive analysis of the value chain, providing information on the different phases and stakeholders involved in the big data market.

Key Questions Answered in this Report

  • How many companies are currently engaged in big data market?
  • Which are the leading companies in this market?
  • What factors are likely to influence the evolution of this market?
  • What is the current and future market size?
  • What is the CAGR of this market?
  • How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report

  • The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
  • Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
  • The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

Additional Benefits

  • Complimentary Excel Data Packs for all Analytical Modules in the Report
  • 15% Free Content Customization
  • Detailed Report Walkthrough Session with Research Team
  • Free Updated report if the report is 6-12 months old or older

TABLE OF CONTENTS

SECTION I: REPORT OVERVIEW

1. PREFACE

  • 1.1. Introduction
  • 1.2. Market Share Insights
  • 1.3. Key Market Insights
  • 1.4. Report Coverage
  • 1.5. Key Questions Answered
  • 1.6. Chapter Outlines

2. RESEARCH METHODOLOGY

  • 2.1. Chapter Overview
  • 2.2. Research Assumptions
  • 2.3. Database Building
    • 2.3.1. Data Collection
    • 2.3.2. Data Validation
    • 2.3.3. Data Analysis
  • 2.4. Project Methodology
    • 2.4.1. Secondary Research
      • 2.4.1.1. Annual Reports
      • 2.4.1.2. Academic Research Papers
      • 2.4.1.3. Company Websites
      • 2.4.1.4. Investor Presentations
      • 2.4.1.5. Regulatory Filings
      • 2.4.1.6. White Papers
      • 2.4.1.7. Industry Publications
      • 2.4.1.8. Conferences and Seminars
      • 2.4.1.9. Government Portals
      • 2.4.1.10. Media and Press Releases
      • 2.4.1.11. Newsletters
      • 2.4.1.12. Industry Databases
      • 2.4.1.13. Roots Proprietary Databases
      • 2.4.1.14. Paid Databases and Sources
      • 2.4.1.15. Social Media Portals
      • 2.4.1.16. Other Secondary Sources
    • 2.4.2. Primary Research
      • 2.4.2.1. Introduction
      • 2.4.2.2. Types
        • 2.4.2.2.1. Qualitative
        • 2.4.2.2.2. Quantitative
      • 2.4.2.3. Advantages
      • 2.4.2.4. Techniques
        • 2.4.2.4.1. Interviews
        • 2.4.2.4.2. Surveys
        • 2.4.2.4.3. Focus Groups
        • 2.4.2.4.4. Observational Research
        • 2.4.2.4.5. Social Media Interactions
      • 2.4.2.5. Stakeholders
        • 2.4.2.5.1. Company Executives (CXOs)
        • 2.4.2.5.2. Board of Directors
        • 2.4.2.5.3. Company Presidents and Vice Presidents
        • 2.4.2.5.4. Key Opinion Leaders
        • 2.4.2.5.5. Research and Development Heads
        • 2.4.2.5.6. Technical Experts
        • 2.4.2.5.7. Subject Matter Experts
        • 2.4.2.5.8. Scientists
        • 2.4.2.5.9. Doctors and Other Healthcare Providers
      • 2.4.2.6. Ethics and Integrity
        • 2.4.2.6.1. Research Ethics
        • 2.4.2.6.2. Data Integrity
    • 2.4.3. Analytical Tools and Databases

3. MARKET DYNAMICS

  • 3.1. Forecast Methodology
    • 3.1.1. Top-Down Approach
    • 3.1.2. Bottom-Up Approach
    • 3.1.3. Hybrid Approach
  • 3.2. Market Assessment Framework
    • 3.2.1. Total Addressable Market (TAM)
    • 3.2.2. Serviceable Addressable Market (SAM)
    • 3.2.3. Serviceable Obtainable Market (SOM)
    • 3.2.4. Currently Acquired Market (CAM)
  • 3.3. Forecasting Tools and Techniques
    • 3.3.1. Qualitative Forecasting
    • 3.3.2. Correlation
    • 3.3.3. Regression
    • 3.3.4. Time Series Analysis
    • 3.3.5. Extrapolation
    • 3.3.6. Convergence
    • 3.3.7. Forecast Error Analysis
    • 3.3.8. Data Visualization
    • 3.3.9. Scenario Planning
    • 3.3.10. Sensitivity Analysis
  • 3.4. Key Considerations
    • 3.4.1. Demographics
    • 3.4.2. Market Access
    • 3.4.3. Reimbursement Scenarios
    • 3.4.4. Industry Consolidation
  • 3.5. Robust Quality Control
  • 3.6. Key Market Segmentations
  • 3.7. Limitations

4. MACRO-ECONOMIC INDICATORS

  • 4.1. Chapter Overview
  • 4.2. Market Dynamics
    • 4.2.1. Time Period
      • 4.2.1.1. Historical Trends
      • 4.2.1.2. Current and Forecasted Estimates
    • 4.2.2. Currency Coverage
      • 4.2.2.1. Overview of Major Currencies Affecting the Market
      • 4.2.2.2. Impact of Currency Fluctuations on the Industry
    • 4.2.3. Foreign Exchange Impact
      • 4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
      • 4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
    • 4.2.4. Recession
      • 4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
      • 4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
    • 4.2.5. Inflation
      • 4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
      • 4.2.5.2. Potential Impact of Inflation on the Market Evolution
    • 4.2.6. Interest Rates
      • 4.2.6.1. Overview of Interest Rates and Their Impact on the Market
      • 4.2.6.2. Strategies for Managing Interest Rate Risk
    • 4.2.7. Commodity Flow Analysis
      • 4.2.7.1. Type of Commodity
      • 4.2.7.2. Origins and Destinations
      • 4.2.7.3. Values and Weights
      • 4.2.7.4. Modes of Transportation
    • 4.2.8. Global Trade Dynamics
      • 4.2.8.1. Import Scenario
      • 4.2.8.2. Export Scenario
    • 4.2.9. War Impact Analysis
      • 4.2.9.1. Russian-Ukraine War
      • 4.2.9.2. Israel-Hamas War
    • 4.2.10. COVID Impact / Related Factors
      • 4.2.10.1. Global Economic Impact
      • 4.2.10.2. Industry-specific Impact
      • 4.2.10.3. Government Response and Stimulus Measures
      • 4.2.10.4. Future Outlook and Adaptation Strategies
    • 4.2.11. Other Indicators
      • 4.2.11.1. Fiscal Policy
      • 4.2.11.2. Consumer Spending
      • 4.2.11.3. Gross Domestic Product (GDP)
      • 4.2.11.4. Employment
      • 4.2.11.5. Taxes
      • 4.2.11.6. R&D Innovation
      • 4.2.11.7. Stock Market Performance
      • 4.2.11.8. Supply Chain
      • 4.2.11.9. Cross-Border Dynamics

SECTION II: QUALITATIVE INSIGHTS

5. EXECUTIVE SUMMARY

6. INTRODUCTION

  • 6.1. Chapter Overview
  • 6.2. Overview of Big Data Market
    • 6.2.1. Type of Component
    • 6.2.2. Areas of Application
    • 6.2.3. Type of Industry Vertical
    • 6.2.4. Type of Delivery Mode
    • 6.2.5. Type of Technology
    • 6.2.6. Type of Business Function
  • 6.3. Future Perspective

7. REGULATORY SCENARIO

SECTION III: MARKET OVERVIEW

8. COMPREHENSIVE DATABASE OF LEADING PLAYERS

9. COMPETITIVE LANDSCAPE

  • 9.1. Chapter Overview
  • 9.2. Big Data: Overall Market Landscape
    • 9.2.1. Analysis by Year of Establishment
    • 9.2.2. Analysis by Company Size
    • 9.2.3. Analysis by Location of Headquarters
    • 9.2.4. Analysis by Ownership Structure

10. WHITE SPACE ANALYSIS

11. COMPANY COMPETITIVENESS ANALYSIS

12. STARTUP ECOSYSTEM IN THE BIG DATA MARKET

  • 12.1. Big Data Market: Market Landscape of Startups
    • 12.1.1. Analysis by Year of Establishment
    • 12.1.2. Analysis by Company Size
    • 12.1.3. Analysis by Company Size and Year of Establishment
    • 12.1.4. Analysis by Location of Headquarters
    • 12.1.5. Analysis by Company Size and Location of Headquarters
    • 12.1.6. Analysis by Ownership Structure
  • 12.2. Key Findings

SECTION IV: COMPANY PROFILES

13. COMPANY PROFILES

  • 13.1. Chapter Overview
  • 13.2. Accenture*
    • 13.2.1. Company Overview
    • 13.2.2. Company Mission
    • 13.2.3. Company Footprint
    • 13.2.4. Management Team
    • 13.2.5. Contact Details
    • 13.2.6. Financial Performance
    • 13.2.7. Operating Business Segments
    • 13.2.8. Service / Product Portfolio (project specific)
    • 13.2.9. MOAT Analysis
    • 13.2.10. Recent Developments and Future Outlook
  • 13.3. Alteryx
  • 13.4. Amazon Web Services
  • 13.5. Cloudera
  • 13.6. Cisco Systems
  • 13.7. Dell
  • 13.8. EMC
  • 13.9. Equifax
  • 13.10. Fair Issac
  • 13.11. Firebolt
  • 13.12. Google
  • 13.13. Hitachi
  • 13.14. IBM
  • 13.15. Informatica
  • 13.16. Microsoft
  • 13.17. Mu Sigma
  • 13.18. Oracle
  • 13.19. OPERA

SECTION V: MARKET TRENDS

14. MEGA TRENDS ANALYSIS

15. UNMET NEED ANALYSIS

16. PATENT ANALYSIS

17. RECENT DEVELOPMENTS

  • 17.1. Chapter Overview
  • 17.2. Recent Funding
  • 17.3. Recent Partnerships
  • 17.4. Other Recent Initiatives

SECTION VI: MARKET OPPORTUNITY ANALYSIS

18. GLOBAL BIG DATA MARKET

  • 18.1. Chapter Overview
  • 18.2. Key Assumptions and Methodology
  • 18.3. Trends Disruption Impacting Market
  • 18.4. Demand Side Trends
  • 18.5. Supply Side Trends
  • 18.6. Global Big data market, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 18.7. Multivariate Scenario Analysis
    • 18.7.1. Conservative Scenario
    • 18.7.2. Optimistic Scenario
  • 18.8. Investment Feasibility Index
  • 18.9. Key Market Segmentations

19. MARKET OPPORTUNITIES BASED ON TYPE OF COMPONENT

  • 19.1. Chapter Overview
  • 19.2. Key Assumptions and Methodology
  • 19.3. Revenue Shift Analysis
  • 19.4. Market Movement Analysis
  • 19.5. Penetration-Growth (P-G) Matrix
  • 19.6. Big data market for Hardware: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.7. Big data market for Services: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.8. Big data market for Software: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 19.9. Data Triangulation and Validation
    • 19.9.1. Secondary Sources
    • 19.9.2. Primary Sources
    • 19.9.3. Statistical Modeling

20. MARKET OPPORTUNITIES BASED ON AREAS OF APPLICATION

  • 20.1. Chapter Overview
  • 20.2. Key Assumptions and Methodology
  • 20.3. Revenue Shift Analysis
  • 20.4. Market Movement Analysis
  • 20.5. Penetration-Growth (P-G) Matrix
  • 20.6. Big Data Market for Advanced Analytics: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 20.7. Big Data Market for Data Discovery and Visualization: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 20.8. Big Data Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 20.9. Data Triangulation and Validation
    • 20.9.1. Secondary Sources
    • 20.9.2. Primary Sources
    • 20.9.3. Statistical Modeling

21. MARKET OPPORTUNITIES BASED ON TYPE OF INDUSTRY VERTICAL

  • 21.1. Chapter Overview
  • 21.2. Key Assumptions and Methodology
  • 21.3. Revenue Shift Analysis
  • 21.4. Market Movement Analysis
  • 21.5. Penetration-Growth (P-G) Matrix
  • 21.6. Big Data Market for Aerospace & Defense: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.7. Big Data Market for BFSI: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.8. Big Data Market for Energy & Power: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.9. Big Data Market for Engineering & Construction: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.10. Big Data Market for Healthcare & Pharmaceuticals: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.11. Big Data Market for Manufacturing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.12. Big Data Market for Media and Entertainment: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.13. Big Data Market for Retail: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.14. Big Data Market for Telecom & IT: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.15. Big Data Market for Transportation & Logistics: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 21.16. Data Triangulation and Validation
    • 21.16.1. Secondary Sources
    • 21.16.2. Primary Sources
    • 21.16.3. Statistical Modeling

22. MARKET OPPORTUNITIES BASED ON TYPE OF DELIVERY MODE

  • 22.1. Chapter Overview
  • 22.2. Key Assumptions and Methodology
  • 22.3. Revenue Shift Analysis
  • 22.4. Market Movement Analysis
  • 22.5. Penetration-Growth (P-G) Matrix
  • 22.6. Big Data Market for Cloud: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.7. Big Data Market for On-Premises: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 22.8. Data Triangulation and Validation
    • 22.8.1. Secondary Sources
    • 22.8.2. Primary Sources
    • 22.8.3. Statistical Modeling

23. MARKET OPPORTUNITIES BASED ON TYPE OF TECHNOLOGY

  • 23.1. Chapter Overview
  • 23.2. Key Assumptions and Methodology
  • 23.3. Revenue Shift Analysis
  • 23.4. Market Movement Analysis
  • 23.5. Penetration-Growth (P-G) Matrix
  • 23.6. Big Data Market for Analytics: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.7. Big Data Market for Database: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.8. Big Data Market for Distribution Tools: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.9. Big Data Market for Machines Learning and Hadoop: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.10. Big Data Market for Predictive Analytics: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.11. Big Data Market for Visualization: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.12. Big Data Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 23.13. Data Triangulation and Validation
    • 23.13.1. Secondary Sources
    • 23.13.2. Primary Sources
    • 23.13.3. Statistical Modeling

24. MARKET OPPORTUNITIES BASED ON TYPE OF BUSINESS FUNCTION

  • 24.1. Chapter Overview
  • 24.2. Key Assumptions and Methodology
  • 24.3. Revenue Shift Analysis
  • 24.4. Market Movement Analysis
  • 24.5. Penetration-Growth (P-G) Matrix
  • 24.6. Big Data Market for Finance: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.7. Big Data Market for Human Resources: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.8. Big Data Market for Marketing and Sales: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.9. Big Data Market for Operations: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 24.10. Data Triangulation and Validation
    • 24.10.1. Secondary Sources
    • 24.10.2. Primary Sources
    • 24.10.3. Statistical Modeling

25. MARKET OPPORTUNITIES BASED ON COMPANY SIZE

  • 25.1. Chapter Overview
  • 25.2. Key Assumptions and Methodology
  • 25.3. Revenue Shift Analysis
  • 25.4. Market Movement Analysis
  • 25.5. Penetration-Growth (P-G) Matrix
  • 25.6. Big Data Market for Large Enterprises: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.7. Big Data Market for Small and Medium Enterprises: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 25.8. Data Triangulation and Validation
    • 25.8.1. Secondary Sources
    • 25.8.2. Primary Sources
    • 25.8.3. Statistical Modeling

26. MARKET OPPORTUNITIES FOR BIG DATA IN NORTH AMERICA

  • 26.1. Chapter Overview
  • 26.2. Key Assumptions and Methodology
  • 26.3. Revenue Shift Analysis
  • 26.4. Market Movement Analysis
  • 26.5. Penetration-Growth (P-G) Matrix
  • 26.6. Big Data Market in North America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.1. Big Data Market in the US: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.2. Big Data Market in Canada: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.3. Big Data Market in Mexico: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 26.6.4. Big Data Market in Other North American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 26.7. Data Triangulation and Validation

27. MARKET OPPORTUNITIES FOR BIG DATA IN EUROPE

  • 27.1. Chapter Overview
  • 27.2. Key Assumptions and Methodology
  • 27.3. Revenue Shift Analysis
  • 27.4. Market Movement Analysis
  • 27.5. Penetration-Growth (P-G) Matrix
  • 27.6. Big Data Market in Europe: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.1. Big Data Market in Austria: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.2. Big Data Market in Belgium: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.3. Big Data Market in Denmark: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.4. Big Data Market in France: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.5. Big Data Market in Germany: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.6. Big Data Market in Ireland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.7. Big Data Market in Italy: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.8. Big Data Market in Netherlands: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.9. Big Data Market in Norway: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.10. Big Data Market in Russia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.11. Big Data Market in Spain: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.12. Big Data Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.13. Big Data Market in Switzerland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.14. Big Data Market in the UK: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 27.6.15. Big Data Market in Other European Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 27.7. Data Triangulation and Validation

28. MARKET OPPORTUNITIES FOR BIG DATA IN ASIA

  • 28.1. Chapter Overview
  • 28.2. Key Assumptions and Methodology
  • 28.3. Revenue Shift Analysis
  • 28.4. Market Movement Analysis
  • 28.5. Penetration-Growth (P-G) Matrix
  • 28.6. Big Data Market in Asia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.1. Big Data Market in China: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.2. Big Data Market in India: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.3. Big Data Market in Japan: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.4. Big Data Market in Singapore: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.5. Big Data Market in South Korea: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 28.6.6. Big Data Market in Other Asian Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 28.7. Data Triangulation and Validation

29. MARKET OPPORTUNITIES FOR BIG DATA IN MIDDLE EAST AND NORTH AFRICA (MENA)

  • 29.1. Chapter Overview
  • 29.2. Key Assumptions and Methodology
  • 29.3. Revenue Shift Analysis
  • 29.4. Market Movement Analysis
  • 29.5. Penetration-Growth (P-G) Matrix
  • 29.6. Big Data Market in Middle East and North Africa (MENA): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.1. Big Data Market in Egypt: Historical Trends (Since 2019) and Forecasted Estimates (Till 205)
    • 29.6.2. Big Data Market in Iran: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.3. Big Data Market in Iraq: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.4. Big Data Market in Israel: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.5. Big Data Market in Kuwait: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.6. Big Data Market in Saudi Arabia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.7. Big Data Market in United Arab Emirates (UAE): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 29.6.8. Big Data Market in Other MENA Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 29.7. Data Triangulation and Validation

30. MARKET OPPORTUNITIES FOR BIG DATA IN LATIN AMERICA

  • 30.1. Chapter Overview
  • 30.2. Key Assumptions and Methodology
  • 30.3. Revenue Shift Analysis
  • 30.4. Market Movement Analysis
  • 30.5. Penetration-Growth (P-G) Matrix
  • 30.6. Big Data Market in Latin America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 30.6.1. Big Data Market in Argentina: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 30.6.2. Big Data Market in Brazil: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 30.6.3. Big Data Market in Chile: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 30.6.4. Big Data Market in Colombia Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 30.6.5. Big Data Market in Venezuela: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 30.6.6. Big Data Market in Other Latin American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
  • 30.7. Data Triangulation and Validation

31. MARKET OPPORTUNITIES FOR BIG DATA IN REST OF THE WORLD

  • 31.1. Chapter Overview
  • 31.2. Key Assumptions and Methodology
  • 31.3. Revenue Shift Analysis
  • 31.4. Market Movement Analysis
  • 31.5. Penetration-Growth (P-G) Matrix
  • 31.6. Big Data Market in Rest of the World: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 31.6.1. Big Data Market in Australia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 31.6.2. Big Data Market in New Zealand: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
    • 31.6.3. Big Data Market in Other Countries
  • 31.7. Data Triangulation and Validation

32. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS

  • 32.1. Leading Player 1
  • 32.2. Leading Player 2
  • 32.3. Leading Player 3
  • 32.4. Leading Player 4
  • 32.5. Leading Player 5
  • 32.6. Leading Player 6
  • 32.7. Leading Player 7
  • 32.8. Leading Player 8

33. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

34. KEY WINNING STRATEGIES

35. PORTER'S FIVE FORCES ANALYSIS

36. SWOT ANALYSIS

37. VALUE CHAIN ANALYSIS

38. ROOTS STRATEGIC RECOMMENDATIONS

  • 38.1. Chapter Overview
  • 38.2. Key Business-related Strategies
    • 38.2.1. Research & Development
    • 38.2.2. Product Manufacturing
    • 38.2.3. Commercialization / Go-to-Market
    • 38.2.4. Sales and Marketing
  • 38.3. Key Operations-related Strategies
    • 38.3.1. Risk Management
    • 38.3.2. Workforce
    • 38.3.3. Finance
    • 38.3.4. Others

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

39. INSIGHTS FROM PRIMARY RESEARCH

40. REPORT CONCLUSION

SECTION IX: APPENDIX

41. TABULATED DATA

42. LIST OF COMPANIES AND ORGANIZATIONS

43. CUSTOMIZATION OPPORTUNITIES

44. ROOTS SUBSCRIPTION SERVICES

45. AUTHOR DETAILS