Cover Image
市場調查報告書

全球巨量資料市場:2015-2020年

Global Big Data Market 2015-2020

出版商 NOVONOUS 商品編碼 341917
出版日期 內容資訊 英文 175 Pages
商品交期: 最快1-2個工作天內
價格
Back to Top
全球巨量資料市場:2015-2020年 Global Big Data Market 2015-2020
出版日期: 2015年09月25日 內容資訊: 英文 175 Pages
簡介

全球巨量資料市場估計到2020年以36.67%的年複合成長率成長。這個成長主要是由於各種部門的巨量資料的普及擴大,分析服務的增加,及對終端用戶來說合適的巨量資料解決方案、服務的可用性。

本報告提供全球巨量資料市場現狀與今後展望的相關調查、各市場區隔/工業/地區的市場分析與預測、成長因素和阻礙要素、價值鏈、參與企業簡介與競爭趨勢,及策略性建議等彙整資料。

第1章 摘要整理

第2章 全球巨量資料市場概要

  • 巨量資料是什麼?
  • 巨量資料的分類
  • 巨量資料的重要性
  • 巨量資料技術

第3章 巨量資料的需求

  • 製造部門的巨量資料的需求
  • 醫療部門的巨量資料的需求
  • 零售部門的巨量資料的需求
  • 通訊部門的巨量資料的需求
  • 金融部門的巨量資料的需求
  • 石油、天然氣部門的巨量資料的需求
  • 教育部門的巨量資料的需求
  • 政府、國防部門的巨量資料的需求

第4章 全球巨量資料市場區隔、預測

  • 整體全球巨量資料市場預測
  • 按全球巨量資料市場區隔預測
    • 伺服器
    • 儲存
    • 網路
    • 軟體
    • 服務
  • 全球巨量資料市場各產業預測
    • 教育
    • 金融
    • 政府、國防
    • 醫療
    • 製造
    • 石油、天然氣
    • 零售
    • 通訊
  • 全球巨量資料市場各地區預測
    • 亞太地區
    • 歐洲
    • 中東、非洲
    • 北美
    • 南美

第5章 全球巨量資料市場上主要成長促進因素與阻礙因素

  • 促進要素
  • 阻礙要素

第6章 巨量資料的產業價值鏈

  • 巨量資料顧問
  • 基礎設施供應商
  • 技術推動者
  • 分析供應商
  • 終端用戶

第7章 全球巨量資料市場上主要企業簡介

  • TEG Analytics
    • 企業簡介
    • 巨量資料的價值鏈的TEG Analytics
    • 財務實際成果
    • 產業策略
    • SWOT分析
  • Heckyl Technologies
  • KloudData Inc.
  • Gramener
  • Germin8
  • VIS Networks Pvt. Ltd.
  • Abzooba
  • Fintellix
  • Latentview
  • Indix
  • Analytic-Edge
  • Tookitaki

第8章 競爭環境

第9章 案例研究

  • 製造部門
  • 醫療部門
  • 零售部門
  • 通訊部門
  • 金融部門
  • 石油、天然氣部門
  • 教育部門
  • 政府部門

第10章 分析模式

  • 波特的五力分析
  • 各部門的SWOT分析
    • 製造部門
    • 醫療部門
    • 零售部門
    • 通訊部門
    • 金融部門
    • 教育部門
    • 石油、天然氣部門
    • 政府、國防部門

第11章 策略建議

  • 終端用戶
  • 巨量資料服務供應商
  • 投資者

圖表

目錄
Product Code: NOV0915004

“Global Big Data Market is Expected to Grow at a CAGR rate of 36.67% till 2020.”

NOVONOUS estimates that Global Big Data market will grow at a CAGR of 36.67% by 2020. This growth is mainly due to increasing penetration of big data in various sectors, increase in analytics services and availability of affordable big data solution and services to end users.

Organizations worldwide are turning their attention to Big Data as a useful means to derive insights from the huge amount of data generated from various sources. Technologies such as NoSQL databases and MapReduce/Hadoop frameworks are at the core of the solutions heralding a paradigm shift.

This research found that high investment costs, lack of awareness and novelty have been the main threats for new entrants in the Big Data space. There are a few major players who control the entire value chain. However, many smaller players have mushroomed who provide consulting in the Analytics space. This research also found that most organizations misunderstand Big Data and it is important to educate the end users through face to face interactions.

The main growth driver for the Big Data industry is the sheer volume of data that is being generated across various industries due to changing business environment.

The growth inhibitors for the Big Data industry have been the slowdown in the global economy, a decrease in investment in Research & Development, lack of quality resourcing and unemployment.

Key Findings and Market Trends in Global Big Data Market

  • Big Data Services segment which includes Big Data consulting, Big Data Analytics etc currently controls the largest market share in Global Big Data market. As per NOVONOUS estimates, Global Big Data Services market is expected to grow at a CAGR of 25.65% till 2020.
  • Big Data Software segment controls the second largest market share in Global Big Data market. As per NOVONOUS estimates, Global Big Data Software market is expected to grow at a CAGR of 38% till 2020 and maintain its market share position even in 2020.
  • Big Data Storage segment controls the third largest market share in Global Big Data market. As per NOVONOUS estimates, Global Big Data Storage market is expected to grow at a CAGR of 55.50% till 2020 and become the leader in terms of it's market share position in 2020.
  • In terms of industry verticals, Big Data in Financial industry which currently controls the largest market share in terms of revenue in Global Big Data market. As per NOVONOUS estimates, Global Big Data in Financial Industry market is expected to grow at a CAGR of 35% till 2020. It is also estimated that the amount of investment done by the financial industry in big data will be about 0.02% of their total revenue by 2020.
  • Big Data in Manufacturing industry which currently controls the second largest market share in terms of revenue in Global Big Data market. As per NOVONOUS estimates, Global Big Data in Manufacturing Industry market is expected to grow at a CAGR of 15% till 2020. It is also estimated that the amount of investment done by the manufacturing industry in big data will be about 0.04% of their total revenue by 2020.
  • Big Data in Telecom Industry controls the third largest market share in terms of revenue in Global Big Data market. As per NOVONOUS estimates, Global Big Data in Telecom Industry market is expected to grow at a CAGR of 65% till 2020 and become the leader in terms of it's market share position in 2020. It is also estimated that the amount of investment done by the telecom industry in big data will be about 2.41% of their total revenue by 2020.
  • In terms of geographies, North America Big Data Market controls the largest market share in terms of revenue in Global Big Data market. As per NOVONOUS estimates, North America Big Data market is expected to grow at a CAGR of 35.80% till 2020 and maintain it's market leader position even in 2020.
  • Europe Big Data Market controls the second largest market share in terms of revenue in Global Big Data market. As per NOVONOUS estimates, Europe Big Data market is expected to grow at a CAGR of 36.50% till 2020 and maintain it's market position even in 2020.
  • Asia Pacific Big Data Market controls the third largest market share in terms of revenue in Global Big Data market. As per NOVONOUS estimates, Asia Pacific Big Data market is expected to register second largest growth rate of 43% till 2020 and maintain it's market position even in 2020.
  • As per NOVONOUS estimates, Middle East & Africa Big Data market which currently has the smallest market share in terms of revenue in Global Big Data market is expected to register fastest growth rate of 45.30% till 2020 and improved it's market position in 2020.

Spanning over 175 pages and 106 exhibits, “Global Big Data Market 2015-2020” report presents an in-depth assessment of the Global Big Data market from 2015 till 2020.

The report has detailed company profiles including their position in big data market value chain, financial performance analysis, product and service wise business strategy, SWOT analysis and key customer details for 12 key players in Global market namely TEG Analytics, Heckyl Technologies, KloudData Inc., Gramener, Germin8, VIS Networks Pvt. Ltd., Abzooba, Fintellix, Latentview, Indix, Analytic-Edge and Tookitaki.

Scope of Global Big Data Market 2015 - 2020 report:

  • This report provides detailed information about Global Big Data market including future forecasts.
  • This report identifies the industry wise need for focusing on Big Data market.
  • This report provides detailed information on growth forecasts for overall Global Big Data market up to 2020.
  • This report provides detailed information on segment wise (servers, storage, networking, software and services) growth forecasts for Global Big Data market up to 2020.
  • This report provides detailed information on industry wise (manufacturing, oil & gas, retail, financial, healthcare, education and government & defense) growth forecasts for Global Big Data market up to 2020.
  • This report provides detailed information on geography wise (asia pacific, europe, middle east & africa, north america and south america) growth forecasts for Global Big Data market up to 2020.
  • The report identifies the growth drivers and inhibitors for Global Big Data market.
  • This study also identifies various parts of Big Data value chain.
  • This report has detailed profiles 12 key players in Global Big Data market covering their business strategy, financial performance, future forecasts and SWOT analysis.
  • This report covers the competitive landscape in Global Big Data market.
  • This report has industry wise (manufacturing, oil & gas, retail, financial, healthcare, education and government & defense) case studies for Big Data.
  • This report provides Porter's Five Forces analysis for Global Big Data market.
  • This report provides industry wise (manufacturing, oil & gas, retail, financial, healthcare, education and government & defense) SWOT (strengths, weakness, opportunities and threats) analysis for Global Big Data market.
  • This report also provides strategic recommendations for end users, Big Data service providers and investors.

Table of Contents

1. Executive Summary

  • Scope of Global Big Data Market 2015-2020 Report
  • Research Methodology

2. Global Big Data Market - Overview

  • 2.1. What is Big Data?
  • 2.2. Big Data Categories
  • 2.3. Importance of Big Data
  • 2.4. Big Data Technology

3. Need for Big Data

  • 3.1. Need for Big Data in Manufacturing Sector
    • 3.1.1. Tracking Business Volume
    • 3.1.2. Understanding the variety
    • 3.1.3. Velocity with which data travels
    • 3.1.4. Understanding veracity of business reporting
    • 3.1.5. Realizing Business Value
  • 3.2. Need for Big Data in Healthcare Sector
    • 3.2.1. Tracking Business Volume
    • 3.2.2. Understanding Variety
    • 3.2.3. Velocity of data
    • 3.2.4. Understanding Veracity of Business Reporting
    • 3.2.5. Realizing business value
  • 3.3. Need for Big Data in Retail Sector
    • 3.3.1. Tracking Business Volume
    • 3.3.2. Understanding Variety
    • 3.3.3. Velocity with which Data Travels
    • 3.3.4. Understanding veracity of business reporting
    • 3.3.5. Realizing the business value
  • 3.4. Need for Big Data in Telecommunications Sector
    • 3.4.1. Big Data Challenges for Today's Telecommunications Provider
    • 3.4.2. Maximizing the Telecom Industry's Return on Big Data
      • 3.4.2.1. Handle large volumes of data
      • 3.4.2.2. Utilize the Variety of Data
      • 3.4.2.3. Manage the Complexity of Data
      • 3.4.2.4. Monetize Data Assets for Business Transformation
  • 3.5. Need for Big Data in Financial Sector
    • 3.5.1. Customer Analytics
    • 3.5.2. Scalability
    • 3.5.3. Gain insights from existing and new sources of internal data
    • 3.5.4. Need for Strong Analytic Capabilities
  • 3.6. Need for Big Data in Oil & Gas Sector
    • 3.6.1. Oil Exploration and Discovery
    • 3.6.2. Enhanced oil exploration
    • 3.6.3. New oil prospect identification
    • 3.6.4. Seismic trace identification
    • 3.6.5. Better Oil Production
    • 3.6.6. Reservoir Engineering
    • 3.6.7. Equipment Maintenance
    • 3.6.8. Security
    • 3.6.9. Safety and Environment
  • 3.7. Need for Big Data in Education Sector
    • 3.7.1. Need for Big Data by Educational Institutions
    • 3.7.2. Big Data in Education - Process
    • 3.7.3. Five Benefits That Big Data Offer to eLearning Professionals
    • 3.7.4. How Big Data Will Impact the Future of e-Learning
  • 3.8. Need for Big Data in Government & Defense Sector
    • 3.8.1. Tracking Business Volume
    • 3.8.2. Understanding Variety
    • 3.8.3. Velocity of data
    • 3.8.4. Understanding Veracity of Business Reporting
    • 3.8.5. Realizing business value

4. Market Segments & Forecast for Global Big Data Market

  • 4.1. Forecast for Overall Global Big Data Market 2015-2020
  • 4.2. Segment Wise Forecast for Global Big Data Market 2015-2020
    • 4.2.1. Servers
    • 4.2.2. Storage
    • 4.2.3. Networking
    • 4.2.4. Software
    • 4.2.5. Services
  • 4.3. Industry Wise Forecast for Global Big Data Market 2015-2020
    • 4.3.1. Education
    • 4.3.2. Financial
    • 4.3.3. Government & Defense
    • 4.3.4. Healthcare
    • 4.3.5. Manufacturing
    • 4.3.6. Oil & Gas
    • 4.3.7. Retail
    • 4.3.8. Telecom
  • 4.4. Geography Wise Forecast for Global Big Data Market 2015-2020
    • 4.4.1. Asia Pacific Big Data Market Forecast 2015-2020
      • 4.4.1.1. Current Economic Situation
        • 4.4.1.1.1. Key Countries (India, South Korea, Japan, China, Australia)
      • 4.4.1.2. Adoption of Big Data Rate
      • 4.4.1.3. Key Market Drivers and Inhibitors
      • 4.4.1.4. Emerging Trends
    • 4.4.2. Europe Big Data Market Forecast 2015-2020
      • 4.4.2.1. Current Economic Situation
        • 4.4.2.1.1. Key Countries (UK, France, Germany etc.)
      • 4.4.2.2. Adoption of Big Data Rate
      • 4.4.2.3. Key Market Drivers and Inhibitors
      • 4.4.2.4. Emerging Trends
    • 4.4.3. Middle East & Africa Big Data Market Forecast 2015-2020
      • 4.4.3.1. Current Economic Situation
        • 4.4.3.1.1. Key Countries (UAE, Iran, Kuwait, Zimbabwe, South Africa, Nigeria etc.)
      • 4.4.3.2. Adoption of Big Data Rate
      • 4.4.3.3. Key Market Drivers and Inhibitors
      • 4.4.3.4. Emerging Trends
    • 4.4.4. North America Big Data Market Forecast 2015-2020
      • 4.4.4.1. Current Economic Situation
      • 4.4.4.2. Adoption of big data rate
      • 4.4.4.3. Key market drivers
      • 4.4.4.4. Emerging Trends
    • 4.4.5. South America Big Data Market Forecast 2015-2020
      • 4.4.5.1. Current Economic Situation
        • 4.4.5.1.1. Key Countries
          • Brazil
          • Mexico
          • Argentina, Chile, Colombia, and Peru
      • 4.4.5.2. Adoption Rate of Big Data
      • 4.4.5.3. Key Market Drivers and Inhibitors
      • 4.4.5.4. Emerging Trends

5. Growth Drivers and Inhibitors for Global Big Data Market

  • 5.1. Growth Drivers
  • 5.2. Growth Inhibitors

6. Big Data Industry Value Chain

  • 6.1. Big Data Consultants
  • 6.2. Infrastructure Providers
  • 6.3. Technology Enablers
  • 6.4. Analytics Providers
  • 6.5. End Users

7. Profile of Key Players in Global Big Data Market

  • 7.1. TEG Analytics
    • 7.1.1. Company profile
    • 7.1.2. TEG Analytics in Big Data Value Chain
    • 7.1.3. Financial Performance of TEG Analytics
    • 7.1.4. Business Strategy
      • 7.1.4.1. Service Level Business Strategy
    • 7.1.5. SWOT Analysis for TEG Analytics
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 7.1.6. Key Customers
  • 7.2. Heckyl Technologies
    • 7.2.1. Company profile
    • 7.2.2. Heckyl Technologies in Big Data Value Chain
    • 7.2.3. Financial Performance of Heckyl Technologies
    • 7.2.4. Business Strategy
      • 7.2.4.1. Product Level Business Strategy
      • 7.2.4.2. Service Level Business Strategy
    • 7.2.5. SWOT Analysis for Heckyl Technologies
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 7.2.6. Key Customers
  • 7.3. KloudData Inc.
    • 7.3.1. Company profile
    • 7.3.2. KloudData in Big Data Value Chain
    • 7.3.3. Financial Performance of KloudData
    • 7.3.4. Business Strategy
      • 7.3.4.1. Product Level Business Strategy
      • 7.3.4.2. Service Level Business Strategy
    • 7.3.5. SWOT Analysis for KloudData
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
  • 7.4. Gramener
    • 7.4.1. Company profile
    • 7.4.2. Gramener in Big Data Value Chain
    • 7.4.3. Business Strategy
      • 7.4.3.1. Product Level Business Strategy
      • 7.4.3.2. Service Level Business Strategy
    • 7.4.4. SWOT Analysis for Gramener
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 7.4.5. Key Customers
  • 7.5. Germin8
    • 7.5.1. Company profile
    • 7.5.2. Germin8 in Big Data Value Chain
    • 7.5.3. Business Strategy
      • 7.5.3.1. Product Level Business Strategy
      • 7.5.3.2. Service Level Business Strategy
    • 7.5.4. SWOT Analysis for Germin8
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 7.5.5. Key Customers
  • 7.6. VIS Networks Pvt. Ltd.
    • 7.6.1. Company Profile
    • 7.6.2. VIS Networks Pvt. Ltd. in the Big Data & Analytics Value Chain
    • 7.6.3. Financial Performance for VIS Networks Pvt. Ltd.
    • 7.6.4. Business Strategy
      • 7.6.4.1. Product Level Strategy
      • 7.6.4.2. Service Level Strategy
    • 7.6.5. SWOT Analysis for VIS Networks Pvt. Ltd.
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
  • 7.7. Abzooba
    • 7.7.1. Company profile
    • 7.7.2. Abzooba in Big Data Value Chain
    • 7.7.3. Financial Performance of Abzooba
    • 7.7.4. Business Strategy
      • 7.7.4.1. Product Level Business Strategy
  • 7.7.4.2. Service Level Business Strategy
    • 7.7.5. SWOT Analysis for Abzooba
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
  • 7.8. Fintellix
    • 7.8.1. Company profile
    • 7.8.2. Fintellix in Big Data Value Chain
    • 7.8.3. Financial Performance of Fintellix
    • 7.8.4. Business Strategy
      • 7.8.4.1. Product Level Business Strategy
      • 7.8.4.2. Service Level Business Strategy
    • 7.8.5. SWOT Analysis for Fintellix
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 7.8.6. Key Customers
  • 7.9. Latentview
    • 7.9.1. Company profile
    • 7.9.2. Latentview in Big Data Value Chain
    • 7.9.3. Business Strategy
      • 7.9.3.1. Product Level Business Strategy
      • 7.9.3.2. Service Level Business Strategy
    • 7.9.4. SWOT Analysis for LatentView
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 7.9.5. Key Customers
  • 7.10. Indix
    • 7.10.1. Company profile
    • 7.10.2. Indix in Big Data Value Chain
    • 7.10.3. Business Strategy
      • 7.10.3.1. Product Level Business Strategy
      • 7.10.3.2. Service Level Business Strategy
    • 7.10.4. SWOT Analysis for Indix
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
  • 7.11. Analytic-Edge
    • 7.11.1. Company Profile
    • 7.11.2. Analytic-Edge Pvt. Ltd. in the Big Data & Analytics Value Chain
    • 7.11.3. Business Strategy
      • 7.11.3.1. Product Level Strategy
      • 7.11.3.2. Service Level Strategy
    • 7.11.4. SWOT Analysis for Analytic-Edge
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 7.11.6. Key Customers
  • 7.12. Tookitaki
    • 7.12.1. Company profile
    • 7.12.2. Tookitaki in Big Data Value Chain
    • 7.12.3. Business Strategy
      • 7.12.3.1. Service Level Business Strategy
      • 7.12.4. SWOT Analysis for Tookitaki
        • Strengths
        • Weaknesses
        • Opportunities
        • Threats
    • 7.12.5. Key Customers

8. Competitive Landscape

  • Collect the right kind of data
  • Start with a plan

9. Case studies

  • 9.1. Manufacturing Sector
    • 9.1.1. Delivering Real Time Analytics in A War Zone for Boeing
  • 9.2. Healthcare Sector
    • Attracting Global Patient
    • New Level of Personalized care
    • Big Data supporting smarter decisions
    • 9.3. Retail Sector
    • About the Case
    • Problem Recognition
  • 9.4. Telecom Sector
    • Case
  • 9. 5. Financial Sector
    • Strategy/execution
    • Results
  • 9.6. Oil & Gas Sector
  • 9.7. Education Sector
    • Informatics
    • Business Situation
    • Problem Recognition
    • Solution Approach
    • Benefits and Results
  • 9.8. Government Sector
    • History

10. Analysis Models

  • 10.1. Porter's Five Forces Analysis of Global Big Data Market
    • Threat of new entrants
    • Bargaining Power of Suppliers
    • Threat of Substitutes
    • Rivalry among Existing Firms
    • Bargaining Power of Buyers
  • 10.2. Sector Wise SWOT Analysis of Global Big Data Market
    • 10.2.1. SWOT Analysis for Big Data in Manufacturing Sector
      • Strengths
      • Weakness
      • Opportunities
      • Threats
    • 10.2.2. SWOT Analysis for Big Data in Healthcare Sector
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 10.2.3. SWOT Analysis for Big Data in Retail Sector
      • Strengths
      • Opportunities
      • Weakness
      • Threats
    • 10.2.4. SWOT Analysis for Big Data in Telecom Sector
      • Strength
      • Weakness
      • Opportunities
      • Threats
    • 10.2.5. SWOT Analysis for Big Data in Financial Sector
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 10.2.6. SWOT Analysis for Big Data in Education Sector
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
    • 10.2.7. SWOT Analysis for Big Data in Oil & Gas Sector
      • Strength
      • Weakness
      • Opportunities
      • Threats
    • 10.2.8. SWOT Analysis for Big Data in Government & Defense Sector
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats

11. Strategic Recommendations

  • For End Users
  • For Big Data Service Providers
  • For Investors
  • List of Exhibits
  • Notes
  • Company Information

List of Exhibits

  • Exhibit 2.1: Big Data Scenario
  • Exhibit 2.2: Big Data Categories
  • Exhibit 2.3: Big Data Categories across the Globe
  • Exhibit 2.4: Industry-wise Usage of Big Data
  • Exhibit 2.5: Big Data Applications
  • Exhibit 2.6: Various Big Data Applications and Examples
  • Exhibit 2.7: Processing in MapReduce and Hadoop
  • Exhibit 4.1: Forecast for Overall Global Big Data Market 2015-2020(in US$ billion)
  • Exhibit 4.2: Market Share of Various Segments in Global Big Data Market(in %)
  • Exhibit 4.3: Segment Wise CAGR Growth Forecast for Global Big Data Market 2015-20(in %)
  • Exhibit 4.4: Forecast for Global Big Data Servers Market 2015-2020(in US$ billion)
  • Exhibit 4.5: Forecast for Global Big Data Storage Market 2015-2020(in US$ billion)
  • Exhibit 4.6: Forecast for Global Big Data Networking Market 2015-2020(in US$ billion)
  • Exhibit 4.7: Forecast for Global Big Data Software Market 2015-2020(in US$ billion)
  • Exhibit 4.8: Forecast for Global Big Data Services Market 2015-2020(in US$ billion)
  • Exhibit 4.9: Industry Wise Market Share in Global Big Data Market(in %)
  • Exhibit 4.10: Industry Wise CAGR Growth Forecast for Global Big Data Market 2015-20(in %)
  • Exhibit 4.11: Forecast for Global Big Data in Education Market 2015-2020(in US$ billion)
  • Exhibit 4.12: Forecast for Global Big Data in Financial Market 2015-2020(in US$ billion)
  • Exhibit 4.13: Forecast for Global Big Data in Government & Defense Market 2015-2020(in US$ billion)
  • Exhibit 4.14: Forecast for Global Big Data in Healthcare Market 2015-2020(in US$ billion)
  • Exhibit 4.15: Forecast for Global Big Data in Manufacturing Market 2015-2020(in US$ billion)
  • Exhibit 4.16: Forecast for Global Big Data in Oil & Gas Market 2015-2020(in US$ billion)
  • Exhibit 4.17: Forecast for Global Big Data in Retail Market 2015-2020(in US$ billion)
  • Exhibit 4.18: Forecast for Global Big Data in Telecom Market 2015-2020(in US$ billion)
  • Exhibit 4.19: Country Wise Growth of Big Data(in %)
  • Exhibit 4.20: Geography Wise Market Share in Global Big Data Market(in %)
  • Exhibit 4.21: Geography Wise CAGR Growth Forecast for Global Big Data Market 2015-20(in %)
  • Exhibit 4.22: Forecast for Asia Pacific Big Data Market 2015-2020(in US$ billion)
  • Exhibit 4.23: Forecast for Europe Big Data Market 2015-2020(in US$ billion)
  • Exhibit 4.24: Forecast for Middle East & Africa Big Data Market 2015-2020(in US$ billion)
  • Exhibit 4.25: Forecast for North America Big Data Market 2015-2020(in US$ billion)
  • Exhibit 4.26: Forecast for South America Big Data Market 2015-2020(in US$ billion)
  • Exhibit 5.1: Growth Drivers and Inhibitors for Global Big Data Market
  • Exhibit 6.1: Big Data Industry Value Chain
  • Exhibit 7.1.1: Company Profile-TEG Analytics
  • Exhibit 7.1.2: Contact Details - TEG Analytics
  • Exhibit 7.1.3: TEG Analytics in Big Data Value Chain
  • Exhibit 7.1.4: TEG Analytics Revenue from 2009 to 2014(in INR million)
  • Exhibit 7.1.5: Year-wise TEG Analytics Revenue Growth from 2009 to 2014(in %)
  • Exhibit 7.1.6: Estimated TEG Analytics in Revenue from 2013-14 to 2019-20(in INR million)
  • Exhibit 7.1.7: SWOT Analysis of TEG Analytics
  • Exhibit 7.1.8: List of Key Customers of TEG Analytics
  • Exhibit 7.2.1: Company Profile - Heckyl Technologies
  • Exhibit 7.2.2: Contact Details - Heckyl Technologies
  • Exhibit 7.2.3: Heckyl Technologies in Big Data Value Chain
  • Exhibit 7.2.4: Estimated Heckyl Technologoies in Revenue from 2013-14 to 2019-20(in INR million)
  • Exhibit 7.2.7: SWOT Analysis of Heckyl Technologies
  • Exhibit 7.2.6: List of Key Customers of Heckyl Technologoies
  • Exhibit 7.3.1: Company Profile - KloudData
  • Exhibit 7.3.2: Contact Details - KloudData
  • Exhibit 7.3.3: KloudData in Big Data Value Chain
  • Exhibit 7.3.4: Estimated KloudData in Revenue from 2013-14 to 2019-20(in INR million)
  • Exhibit 7.3.5: SWOT Analysis of KloudData
  • Exhibit 7.4.1: Company Profile - Gramener
  • Exhibit 7.4.2: Contact Details - Gramener
  • Exhibit 7.4.3: Gramener in Big Data Value Chain
  • Exhibit 7.4.4: SWOT Analysis of Gramener
  • Exhibit 7.4.5: List of Key Customers of Gramener
  • Exhibit 7.5.1: Company Profile- Germin 8
  • Exhibit 7.5.2: Contact Details - Germin 8
  • Exhibit 7.5.3: Germin8 in Big Data Value Chain
  • Exhibit 7.5.4: SWOT Analysis of Germin8
  • Exhibit 7.5.5: List of Key Customers of Germin 8
  • Exhibit 7.6.1: Company Profile - VIS Networks Pvt. Ltd.
  • Exhibit 7.6.2: Contact Details - VIS Networks Pvt. Ltd.
  • Exhibit 7.6.3: VIS Networks Pvt. Ltd. in the Big Data Value Chain
  • Exhibit 7.6.4: VIS Networks Pvt. Ltd. Revenue from 2012 to 2015(in US$ million)
  • Exhibit 7.6.5: VIS Networks Ltd. Estimated Revenue from 2015 to 2020(in US$ million)
  • Exhibit 7.6.6: SWOT Analysis for VIS Networks Pvt. Ltd.
  • Exhibit 7.7.1: Company Profile - Abzooba
  • Exhibit 7.7.2: Contact Details - Abzooba
  • Exhibit 7.7.3: Abzooba in Big Data Value Chain
  • Exhibit 7.7.4: Estimated Abzooba in Revenue from 2013-14 to 2019-20(in INR million)
  • Exhibit 7.7.5: SWOT Analysis of Abzooba
  • Exhibit 7.8.1: Company Profile - Fintelllix
  • Exhibit 7.8.2: Contact Details - Fintelllix
  • Exhibit 7.8.3: Fintellix in Big Data Value Chain
  • Exhibit 7.8.4: Estimated Fintellix in Revenue from 2013-14 to 2019-20(in INR billion)
  • Exhibit 7.8.5: SWOT Analysis of Fintellix
  • Exhibit 7.9.1: Company Profile - Latentview
  • Exhibit 7.9.2: Contact Details - Latentview
  • Exhibit 7.9.3: latentview in Big Data Value Chain
  • Exhibit 7.9.4: SWOT Analysis of LatentView
  • Exhibit 7.10.1: Company Profile - Indix
  • Exhibit 7.10.2: Contact Details - Indix
  • Exhibit 7.10.3: Indix in Big Data Value Chain
  • Exhibit 7.10.4: SWOT Analysis of Indix
  • Exhibit 7.11.1: Company Profile - Analytic-Edge Pvt. Ltd.
  • Exhibit 7.11.2: Contact Details - Analytic-Edge Pvt. Ltd.
  • Exhibit 7.11.3: Analytic-Edge Pvt. Ltd. in the Big Data Value Chain
  • Exhibit 7.11.4: SWOT Analysis for Analytic-Edge Pvt. Ltd.
  • Exhibit 7.12.1: Company Profile - Tookitaki
  • Exhibit 7.12.2: Contact Details - Tookitaki
  • Exhibit 7.12.3: Tookitaki in Big Data Value Chain
  • Exhibit 7.12.4: SWOT Analysis of Tookitaki
  • Exhibit 9.1: Transaction types and interest rates offered by OCBC
  • Exhibit 10.1: Porter's Five Forces Analysis Model for Global Big Data Market
  • Exhibit 10.2: SWOT Analysis for Big Data in Manufacturing Sector
  • Exhibit 10.3: SWOT Analysis of Big Data in Healthcare Sector
  • Exhibit 10.4: SWOT Analysis for Big Data in Retail Sector
  • Exhibit 10.5: SWOT Analysis for Big Data in Telecom Sector
  • Exhibit 10.6: SWOT Analysis for Big Data in Financial Sector
  • Exhibit 10.7: SWOT Analysis for Big Data in Education Sector
  • Exhibit 10.8: SWOT Analysis for Big Data in Oil & Gas Sector
  • Exhibit 10.9: SWOT Analysis for Big Data in Government and Defense Sector

Companies Covered

  • 1. TEG Analytics
  • 2. Heckyl Technologies
  • 3. KloudData Inc.
  • 4. Gramener
  • 5. Germin8
  • 6. VIS Networks Pvt. Ltd.
  • 7. Abzooba
  • 8. Fintellix
  • 9. Latentview
  • 10. Indix
  • 11. Analytic-Edge
  • 12. Tookitaki
Back to Top