세계의 빅데이터 시장(-2035년) : 구성요소별, 용도별, 산업별, 제공 방식별, 기술별, 업무 기능별, 기업 규모별, 지역별

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

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한글목차

세계 빅데이터 시장 규모는 현재 2,628억 7,000만 달러에서 예측 기간 동안 13.10%의 CAGR(연평균 성장률)을 기록하며 2035년에는 1조 190억 달러 규모로 성장할 것으로 예상됩니다.

빅데이터 시장 기회 : 분류

구성요소

• 하드웨어
• 서비스
• 소프트웨어

용도

• 고급 분석
• 데이터 발견 및 시각화
• 기타

산업

• 항공우주 및 방위
• BFSI
• 에너지 및 전력
• 엔지니어링 및 건설
• 헬스케어 및 제약
• 제조
• 미디어 및 엔터테인먼트
• 소매업
• 통신 및 IT
• 교통 및 물류

제공 방식

• 클라우드
• 온프레미스

기술

• 분석
• 데이터베이스
• 배포 도구
• 머신러닝과 Hadoop
• 예측 분석
• 시각화
• 기타

업무 기능

• 재무
• 인사
• 마케팅 및 영업
• 오퍼레이션

기업 규모

• 대기업
• 중소기업

지역

• 북미
• 미국
• 캐나다
• 멕시코
• 기타
• 유럽
• 오스트리아
• 벨기에
• 덴마크
• 프랑스
• 독일
• 아일랜드
• 이탈리아
• 네덜란드
• 노르웨이
• 러시아
• 스페인
• 스웨덴
• 스위스
• 영국
• 기타
• 아시아
• 중국
• 인도
• 일본
• 싱가포르
• 한국
• 기타
• 라틴아메리카
• 브라질
• 칠레
• 콜롬비아
• 베네수엘라
• 기타
• 중동 및 북아프리카
• 이집트
• 이란
• 이라크
• 이스라엘
• 쿠웨이트
• 사우디아라비아
• 아랍에미리트
• 기타
• 세계 기타 지역
• 호주
• 뉴질랜드
• 기타

빅데이터 시장 : 성장과 동향

빅데이터는 구조화된 데이터, 비정형 데이터, 또는 반구조화된 데이터의 매우 큰 집합을 의미하며, 이는 시간이 지남에 따라 지속적으로 증가합니다. 기존 데이터 관리 시스템에서는 이러한 방대한 데이터세트의 속도, 양, 다양성, 복잡성으로 인해 저장 및 처리가 어렵고, 기존 시스템에서는 분석 및 인사이트 추출에 어려움을 겪고 있습니다. 기업 활동의 다양한 측면에서 데이터 수집, 저장, 활용에 대한 중요성이 높아지고, 고급 분석 솔루션과 실시간 처리 등의 트렌드에 대한 수요가 확대됨에 따라 빅데이터 시장은 향후 몇 년간 큰 폭의 성장이 예상됩니다.

또한, AI가 빅데이터 분석과 머신러닝(ML)에 미치는 영향력이 확대됨에 따라 빅데이터와 첨단 툴의 통합을 촉진하여 매우 복잡한 데이터세트를 처리하고 뛰어난 분석적 인사이트를 제공할 수 있을 것으로 기대됩니다. 또한, 클라우드 기반 빅데이터 솔루션과 산업별 분석 도구 등의 분야에서 지속적인 연구개발이 이루어지고 있어 예측 기간 동안 빅데이터 시장은 크게 성장할 것으로 예상됩니다.

세계의 빅데이터 시장을 조사했으며, 시장 개요와 배경, 시장 영향요인 분석, 시장 규모 추정 및 예측, 각종 부문별·지역별 상세 분석, 경쟁 상황, 주요 기업 개요 등의 정보를 전해드립니다.

목차

섹션 I : 보고서 개요

제1장 서문

제2장 조사 방법

제3장 시장 역학

제4장 거시경제 지표

섹션 II : 정성적 인사이트

제5장 주요 요약

제6장 소개

제7장 규제 시나리오

섹션 III : 시장 개요

제8장 주요 기업의 종합적 데이터베이스

제9장 경쟁 구도

제10장 화이트 스페이스 분석

제11장 기업 경쟁력 분석

제12장 빅데이터 시장의 스타트업 생태계

섹션 IV : 기업 개요

제13장 기업 개요

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

섹션 V : 시장 동향

제14장 메가트렌드 분석

제15장 미충족 수요 분석

제16장 특허 분석

제17장 최근 동향

섹션 VI : 시장 기회 분석

제18장 세계의 빅데이터 시장

제19장 구성요소별 시장 기회

제20장 용도별 시장 기회

제21장 산업별 시장 기회

제22장 제공 방식별 시장 기회

제23장 기술별 시장 기회

제24장 업무 기능별 시장 기회

제25장 기업 규모별 시장 기회

제26장 북미의 빅데이터 시장 기회

제27장 유럽의 빅데이터 시장 기회

제28장 아시아의 빅데이터 시장 기회

제29장 중동 및 북아프리카의 빅데이터 시장 기회

제30장 라틴아메리카의 빅데이터 시장 기회

제31장 세계 기타 지역의 빅데이터 시장 기회

제32장 시장 집중 분석 : 주요 기업 분포

제33장 인접 시장 분석

섹션 VII : 전략 툴

제34장 승리의 열쇠가 되는 전략

제35장 Porter's Five Forces 분석

제36장 SWOT 분석

제37장 밸류체인 분석

제38장 ROOTS에 의한 전략 제안

섹션 VIII : 기타 독점적 인사이트

제39장 1차 조사로부터의 인사이트

제40장 보고서 결론

섹션 IX : 부록

제41장 표형식 데이터

제42장 기업·단체 리스트

제43장 맞춤화의 기회

제44장 ROOTS 구독 서비스

제45장 저자 상세

KSM

영문 목차

영문목차

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.

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

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