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市場調查報告書

巨量資料市場:2016-2030年 - 機會·課題·策略·垂直產業·預測

The Big Data Market: 2016 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts

出版商 Signals and Systems Telecom 商品編碼 359713
出版日期 內容資訊 英文 390 Pages
商品交期: 最快1-2個工作天內
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巨量資料市場:2016-2030年 - 機會·課題·策略·垂直產業·預測 The Big Data Market: 2016 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts
出版日期: 2016年06月10日 內容資訊: 英文 390 Pages
簡介

儘管有隱私的疑慮及組織性抗拒方面的課題,巨量資料投資趨勢仍然全球性日益升溫。巨量資料的投資規模,預測2016年將超過460億美元。投資規模預料今後4年將以12%的年複合成長率 (CAGR) 成長。

本報告涵括全球巨量資料市場,提供您巨量資料的生態系統相關之詳細分析,巨量資料硬體設備·軟體·專門服務市場規模的預測,及水平市場·垂直市場·地區·各主要國家的預測等資訊。

第1章 簡介

第2章 巨量資料概要

  • 巨量資料是什麼?
  • 巨量資料處理的主要方法
    • Hadoop
    • NoSQL
    • MPAD (Massively Parallel Analytic Databases)
    • 界內記憶體處理
    • 串流處理技術
    • Spark
    • 其他資料庫·分析技術
  • 巨量資料的主要特徵
    • 數量
    • 速度
    • 多樣性
    • 價值
  • 市場推動成長因素
    • 優點的認識
    • 巨量資料平台的成熟
    • 網站巨大企業·政府·企業的持續性投資
    • 資料數量·速度·多樣性的擴大
    • 供應商的深度參與·合作
    • 技術趨勢降低進入門檻
  • 市場障礙
    • 缺乏分析專家
    • 巨量資料策略不確實
    • 對引進巨量資料的組織性抗拒
    • 技術課題:可擴展性·維修
    • 安全·隱私的疑慮

第3章 巨量資料分析

  • 巨量資料分析是什麼?
  • 分析的重要性
  • 反應活性分析 vs. 專業活性分析
  • 客戶 vs. 運營分析
  • 技術·實行的方法
    • 柵格計算
    • 界內資料庫處理
    • 界內記憶體分析
    • 機器學習·資料探勘
    • 預測分析
    • NLP (自然地語言處理)
    • 文本分析
    • 視覺化分析及工具
    • 社群媒體,IT及Telco (通訊業者) 網路分析

第4章 汽車·航太·運輸的巨量資料

  • 概要·投資可能性
  • 主的應用程式
  • 案例研究

第5章 銀行·安全的巨量資料

第6章 防衛·情報的巨量資料

第7章 教育的巨量資料

第8章 醫療·醫藥品的巨量資料

第9章 智慧城市·智慧型大樓的巨量資料

第10章 保險的巨量資料

第11章 製造業·天然資源的巨量資料

第12章 網站·媒體·娛樂的巨量資料

第13章 公共安全·國防安全保障的巨量資料

第14章 公共服務的巨量資料

第15章 零售·批發·飯店的巨量資料

第16章 通訊的巨量資料

第17章 公共事業·能源的巨量資料

第18章 巨量資料產業發展藍圖·價值鏈

  • 巨量資料產業發展藍圖
  • 巨量資料產業的價值鏈

第19章 標準化·管理方案

  • CSCC (Cloud Standards Customer Council) - Big Data Working Group
  • NIST (National Institute of Standards and Technology) - Big Data Working Group
  • OASIS -Technical Committees
  • ODaF (Open Data Foundation)
  • Open Data Center Alliance
  • CSA (Cloud Security Alliance) - Big Data Working Group
  • ITU (International Telecommunications Union)
  • ISO (International Organization for Standardization) 以及其他

第20章 市場分析·預測

  • 全球巨量資料市場展望
  • 次市場的區分
  • 垂直市場區分
  • 地區展望
  • 亞太地區
  • 東歐
  • 南美·中美
  • 中東·非洲
  • 北美
  • 西歐

第21章 業者情勢

第22章 結論·策略性建議

  • 巨量資料技術:超越資料獲得·分析
  • 從成本中心到盈利中心之IT的轉換
  • 隱私的影響是否將阻礙成功?
  • 法規是否為巨量資料投資帶來負面影響?
  • 組織及數據孤島的戰鬥
  • 軟體 vs. 硬體設備投資
  • 供應商的佔有率:誰將領導市場?
  • 巨量資料促進廣泛的IT產業投資
  • IoT & M2M的影響評估
  • 建議
    • 巨量資料硬體設備,軟體及專門服務供應商
    • 企業

圖表

目錄

"Big Data" originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.

Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to scientific R&D.

Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for over $46 Billion in 2016 alone. These investments are further expected to grow at a CAGR of 12% over the next four years.

The "Big Data Market: 2016 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts" report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2016 through to 2030. The forecasts are further segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report

Table of Contents

1 Chapter 1: Introduction

  • 1.1 Executive Summary
  • 1.2 Topics Covered
  • 1.3 Historical Revenue & Forecast Segmentation
  • 1.4 Key Questions Answered
  • 1.5 Key Findings
  • 1.6 Methodology
  • 1.7 Target Audience
  • 1.8 Companies & Organizations Mentioned

2 Chapter 2: An Overview of Big Data

  • 2.1 What is Big Data?
  • 2.2 Key Approaches to Big Data Processing
    • 2.2.1 Hadoop
    • 2.2.2 NoSQL
    • 2.2.3 MPAD (Massively Parallel Analytic Databases)
    • 2.2.4 In-memory Processing
    • 2.2.5 Stream Processing Technologies
    • 2.2.6 Spark
    • 2.2.7 Other Databases & Analytic Technologies
  • 2.3 Key Characteristics of Big Data
    • 2.3.1 Volume
    • 2.3.2 Velocity
    • 2.3.3 Variety
    • 2.3.4 Value
  • 2.4 Market Growth Drivers
    • 2.4.1 Awareness of Benefits
    • 2.4.2 Maturation of Big Data Platforms
    • 2.4.3 Continued Investments by Web Giants, Governments & Enterprises
    • 2.4.4 Growth of Data Volume, Velocity & Variety
    • 2.4.5 Vendor Commitments & Partnerships
    • 2.4.6 Technology Trends Lowering Entry Barriers
  • 2.5 Market Barriers
    • 2.5.1 Lack of Analytic Specialists
    • 2.5.2 Uncertain Big Data Strategies
    • 2.5.3 Organizational Resistance to Big Data Adoption
    • 2.5.4 Technical Challenges: Scalability & Maintenance
    • 2.5.5 Security & Privacy Concerns

3 Chapter 3: Big Data Analytics

  • 3.1 What are Big Data Analytics?
  • 3.2 The Importance of Analytics
  • 3.3 Reactive vs. Proactive Analytics
  • 3.4 Customer vs. Operational Analytics
  • 3.5 Technology & Implementation Approaches
    • 3.5.1 Grid Computing
    • 3.5.2 In-Database Processing
    • 3.5.3 In-Memory Analytics
    • 3.5.4 Machine Learning & Data Mining
    • 3.5.5 Predictive Analytics
    • 3.5.6 NLP (Natural Language Processing)
    • 3.5.7 Text Analytics
    • 3.5.8 Visual Analytics
    • 3.5.9 Social Media, IT & Telco Network Analytics

4 Chapter 4: Big Data in Automotive, Aerospace & Transportation

  • 4.1 Overview & Investment Potential
  • 4.2 Key Applications
    • 4.2.1 Warranty Analytics for Automotive OEMs
    • 4.2.2 Predictive Aircraft Maintenance & Fuel Optimization
    • 4.2.3 Air Traffic Control
    • 4.2.4 Transport Fleet Optimization
  • 4.3 Case Studies
    • 4.3.1 Boeing: Making Flying More Efficient with Big Data
    • 4.3.2 BMW: Eliminating Defects in New Vehicle Models with Big Data
    • 4.3.3 Toyota Motor Corporation: Powering Smart Cars with Big Data
    • 4.3.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data

5 Chapter 5: Big Data in Banking & Securities

  • 5.1 Overview & Investment Potential
  • 5.2 Key Applications
    • 5.2.1 Customer Retention & Personalized Product Offering
    • 5.2.2 Risk Management
    • 5.2.3 Fraud Detection
    • 5.2.4 Credit Scoring
  • 5.3 Case Studies
    • 5.3.1 HSBC Group: Avoiding Regulatory Penalties with Big Data
    • 5.3.2 JPMorgan Chase & Co.: Improving Business Processes with Big Data
    • 5.3.3 OTP Bank: Reducing Loan Defaults with Big Data
    • 5.3.4 CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data

6 Chapter 6: Big Data in Defense & Intelligence

  • 6.1 Overview & Investment Potential
  • 6.2 Key Applications
    • 6.2.1 Intelligence Gathering
    • 6.2.2 Battlefield Analytics
    • 6.2.3 Energy Saving Opportunities in the Battlefield
    • 6.2.4 Preventing Injuries on the Battlefield
  • 6.3 Case Studies
    • 6.3.1 U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data
    • 6.3.2 Royal Navy: Empowering Submarine Warfare with Big Data
    • 6.3.3 NSA (National Security Agency): Capitalizing on Big Data to Detect Threats
    • 6.3.4 Chinese Ministry of State Security: Predictive Policing with Big Data
    • 6.3.5 French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data

7 Chapter 7: Big Data in Education

  • 7.1 Overview & Investment Potential
  • 7.2 Key Applications
    • 7.2.1 Information Integration
    • 7.2.2 Identifying Learning Patterns
    • 7.2.3 Enabling Student-Directed Learning
  • 7.3 Case Studies
    • 7.3.1 Purdue University: Ensuring Successful Higher Education Outcomes with Big Data
    • 7.3.2 Nottingham Trent University: Successful Student Outcomes with Big Data
    • 7.3.3 Edith Cowen University: Increasing Student Retention with Big Data

8 Chapter 8: Big Data in Healthcare & Pharma

  • 8.1 Overview & Investment Potential
  • 8.2 Key Applications
    • 8.2.1 Managing Population Health Efficiently
    • 8.2.2 Improving Patient Care with Medical Data Analytics
    • 8.2.3 Improving Clinical Development & Trials
    • 8.2.4 Drug Development: Improving Time to Market
  • 8.3 Case Studies
    • 8.3.1 Novartis: Digitizing Healthcare with Big Data
    • 8.3.2 GSK (GlaxoSmithKline): Accelerating Drug Discovering with Big Data
    • 8.3.3 Pfizer: Developing Effective and Targeted Therapies with Big Data
    • 8.3.4 Roche: Personalizing Healthcare with Big Data
    • 8.3.5 Sanofi: Proactive Diabetes Care with Big Data

9 Chapter 9: Big Data in Smart Cities & Intelligent Buildings

  • 9.1 Overview & Investment Potential
  • 9.2 Key Applications
    • 9.2.1 Energy Optimization & Fault Detection
    • 9.2.2 Intelligent Building Analytics
    • 9.2.3 Urban Transportation Management
    • 9.2.4 Optimizing Energy Production
    • 9.2.5 Water Management
    • 9.2.6 Urban Waste Management
  • 9.3 Case Studies
    • 9.3.1 Singapore: Building a Smart Nation with Big Data
    • 9.3.2 Glasgow City Council: Promoting Smart City Efforts with Big Data
    • 9.3.3 OVG Real Estate: Powering the World's Most Intelligent Building with Big Data

10 Chapter 10: Big Data in Insurance

  • 10.1 Overview & Investment Potential
  • 10.2 Key Applications
    • 10.2.1 Claims Fraud Mitigation
    • 10.2.2 Customer Retention & Profiling
    • 10.2.3 Risk Management
  • 10.3 Case Studies
    • 10.3.1 Zurich Insurance Group: Enhancing Risk Management with Big Data
    • 10.3.2 RSA Group: Improving Customer Relations with Big Data
    • 10.3.3 Primerica: Improving Insurance Sales Force Productivity with Big Data

11 Chapter 11: Big Data in Manufacturing & Natural Resources

  • 11.1 Overview & Investment Potential
  • 11.2 Key Applications
    • 11.2.1 Asset Maintenance & Downtime Reduction
    • 11.2.2 Quality & Environmental Impact Control
    • 11.2.3 Optimized Supply Chain
    • 11.2.4 Exploration & Identification of Natural Resources
  • 11.3 Case Studies
    • 11.3.1 Intel Corporation: Cutting Manufacturing Costs with Big Data
    • 11.3.2 Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data
    • 11.3.3 Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data
    • 11.3.4 Brunei: Saving Natural Resources with Big Data

12 Chapter 12: Big Data in Web, Media & Entertainment

  • 12.1 Overview & Investment Potential
  • 12.2 Key Applications
    • 12.2.1 Audience & Advertising Optimization
    • 12.2.2 Channel Optimization
    • 12.2.3 Recommendation Engines
    • 12.2.4 Optimized Search
    • 12.2.5 Live Sports Event Analytics
    • 12.2.6 Outsourcing Big Data Analytics to Other Verticals
  • 12.3 Case Studies
    • 12.3.1 NFL (National Football League): Improving Stadium Experience with Big Data
    • 12.3.2 Walt Disney Company: Enhancing Theme Park Experience with Big Data
    • 12.3.3 Baidu: Reshaping Search Capabilities with Big Data
    • 12.3.4 Constant Contact: Effective Marketing with Big Data

13 Chapter 13: Big Data in Public Safety & Homeland Security

  • 13.1 Overview & Investment Potential
  • 13.2 Key Applications
    • 13.2.1 Cyber Crime Mitigation
    • 13.2.2 Crime Prediction Analytics
    • 13.2.3 Video Analytics & Situational Awareness
  • 13.3 Case Studies
    • 13.3.1 U.S. DHS (Department of Homeland Security): Identifying Threats to Physical and Network Infrastructure with Big Data
    • 13.3.2 Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data
    • 13.3.3 Memphis Police Department: Crime Reduction with Big Data

14 Chapter 14: Big Data in Public Services

  • 14.1 Overview & Investment Potential
  • 14.2 Key Applications
    • 14.2.1 Public Sentiment Analysis
    • 14.2.2 Tax Collection & Fraud Detection
    • 14.2.3 Economic Analysis
  • 14.3 Case Studies
    • 14.3.1 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data
    • 14.3.2 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data
    • 14.3.3 City of Chicago: Improving Government Productivity with Big Data
    • 14.3.4 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data
    • 14.3.5 Ambulance Victoria: Improving Patient Survival Rates with Big Data

15 Chapter 15: Big Data in Retail, Wholesale & Hospitality

  • 15.1 Overview & Investment Potential
  • 15.2 Key Applications
    • 15.2.1 Customer Sentiment Analysis
    • 15.2.2 Customer & Branch Segmentation
    • 15.2.3 Price Optimization
    • 15.2.4 Personalized Marketing
    • 15.2.5 Optimizing & Monitoring the Supply Chain
    • 15.2.6 In-field Sales Analytics
  • 15.3 Case Studies
    • 15.3.1 Walmart: Making Smarter Stocking Decision with Big Data
    • 15.3.2 Tesco: Reducing Supermarket Energy Bills with Big Data
    • 15.3.3 Marriott International: Elevating Guest Services with Big Data
    • 15.3.4 JJ Food Service: Predictive Wholesale Shopping Lists with Big Data

16 Chapter 16: Big Data in Telecommunications

  • 16.1 Overview & Investment Potential
  • 16.2 Key Applications
    • 16.2.1 Network Performance & Coverage Optimization
    • 16.2.2 Customer Churn Prevention
    • 16.2.3 Personalized Marketing
    • 16.2.4 Tailored Location Based Services
    • 16.2.5 Fraud Detection
  • 16.3 Case Studies
    • 16.3.1 BT Group: Hunting Down Nuisance Callers with Big Data
    • 16.3.2 AT&T: Smart Network Management with Big Data
    • 16.3.3 T-Mobile USA: Cutting Down Churn Rate with Big Data
    • 16.3.4 TEOCO: Helping Service Providers Save Millions with Big Data
    • 16.3.5 WIND Mobile: Optimizing Video Quality with Big Data
    • 16.3.6 Coriant: SaaS Based Analytics with Big Data

17 Chapter 17: Big Data in Utilities & Energy

  • 17.1 Overview & Investment Potential
  • 17.2 Key Applications
    • 17.2.1 Customer Retention
    • 17.2.2 Forecasting Energy
    • 17.2.3 Billing Analytics
    • 17.2.4 Predictive Maintenance
    • 17.2.5 Maximizing the Potential of Drilling
    • 17.2.6 Production Optimization
  • 17.3 Case Studies
    • 17.3.1 Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data
    • 17.3.2 British Gas: Improving Customer Service with Big Data
    • 17.3.3 Oncor Electric Delivery: Intelligent Power Grid Management with Big Data

18 Chapter 18: Big Data Industry Roadmap & Value Chain

  • 18.1 Big Data Industry Roadmap
    • 18.1.1 2010 - 2013: Initial Hype and the Rise of Analytics
    • 18.1.2 2014 - 2017: Emergence of SaaS Based Big Data Solutions
    • 18.1.3 2018 - 2020: Growing Adoption of Scalable Machine Learning
    • 18.1.4 2021 & Beyond: Widespread Investments on Cognitive & Personalized Analytics
  • 18.2 The Big Data Value Chain
    • 18.2.1 Hardware Providers
      • 18.2.1.1 Storage & Compute Infrastructure Providers
      • 18.2.1.2 Networking Infrastructure Providers
    • 18.2.2 Software Providers
      • 18.2.2.1 Hadoop & Infrastructure Software Providers
      • 18.2.2.2 SQL & NoSQL Providers
      • 18.2.2.3 Analytic Platform & Application Software Providers
      • 18.2.2.4 Cloud Platform Providers
    • 18.2.3 Professional Services Providers
    • 18.2.4 End-to-End Solution Providers
    • 18.2.5 Vertical Enterprises

19 Chapter 19: Standardization & Regulatory Initiatives

  • 19.1 CSCC (Cloud Standards Customer Council) - Big Data Working Group
  • 19.2 NIST (National Institute of Standards and Technology) - Big Data Working Group
  • 19.3 OASIS -Technical Committees
  • 19.4 ODaF (Open Data Foundation)
  • 19.5 Open Data Center Alliance
  • 19.6 CSA (Cloud Security Alliance) - Big Data Working Group
  • 19.7 ITU (International Telecommunications Union)
  • 19.8 ISO (International Organization for Standardization) and Others

20 Chapter 20: Market Analysis & Forecasts

  • 20.1 Global Outlook of the Big Data Market
  • 20.2 Submarket Segmentation
    • 20.2.1 Storage and Compute Infrastructure
    • 20.2.2 Networking Infrastructure
    • 20.2.3 Hadoop & Infrastructure Software
    • 20.2.4 SQL
    • 20.2.5 NoSQL
    • 20.2.6 Analytic Platforms & Applications
    • 20.2.7 Cloud Platforms
    • 20.2.8 Professional Services
  • 20.3 Vertical Market Segmentation
    • 20.3.1 Automotive, Aerospace & Transportation
    • 20.3.2 Banking & Securities
    • 20.3.3 Defense & Intelligence
    • 20.3.4 Education
    • 20.3.5 Healthcare & Pharmaceutical
    • 20.3.6 Smart Cities & Intelligent Buildings
    • 20.3.7 Insurance
    • 20.3.8 Manufacturing & Natural Resources
    • 20.3.9 Media & Entertainment
    • 20.3.10 Public Safety & Homeland Security
    • 20.3.11 Public Services
    • 20.3.12 Retail, Wholesale & Hospitality
    • 20.3.13 Telecommunications
    • 20.3.14 Utilities & Energy
    • 20.3.15 Other Sectors
  • 20.4 Regional Outlook
  • 20.5 Asia Pacific
    • 20.5.1 Country Level Segmentation
    • 20.5.2 Australia
    • 20.5.3 China
    • 20.5.4 India
    • 20.5.5 Indonesia
    • 20.5.6 Japan
    • 20.5.7 Malaysia
    • 20.5.8 Pakistan
    • 20.5.9 Philippines
    • 20.5.10 Singapore
    • 20.5.11 South Korea
    • 20.5.12 Taiwan
    • 20.5.13 Thailand
    • 20.5.14 Rest of Asia Pacific
  • 20.6 Eastern Europe
    • 20.6.1 Country Level Segmentation
    • 20.6.2 Czech Republic
    • 20.6.3 Poland
    • 20.6.4 Russia
    • 20.6.5 Rest of Eastern Europe
  • 20.7 Latin & Central America
    • 20.7.1 Country Level Segmentation
    • 20.7.2 Argentina
    • 20.7.3 Brazil
    • 20.7.4 Mexico
    • 20.7.5 Rest of Latin & Central America
  • 20.8 Middle East & Africa
    • 20.8.1 Country Level Segmentation
    • 20.8.2 Israel
    • 20.8.3 Qatar
    • 20.8.4 Saudi Arabia
    • 20.8.5 South Africa
    • 20.8.6 UAE
    • 20.8.7 Rest of the Middle East & Africa
  • 20.9 North America
    • 20.9.1 Country Level Segmentation
    • 20.9.2 Canada
    • 20.9.3 USA
  • 20.10 Western Europe
    • 20.10.1 Country Level Segmentation
    • 20.10.2 Denmark
    • 20.10.3 Finland
    • 20.10.4 France
    • 20.10.5 Germany
    • 20.10.6 Italy
    • 20.10.7 Netherlands
    • 20.10.8 Norway
    • 20.10.9 Spain
    • 20.10.10 Sweden
    • 20.10.11 UK
    • 20.10.12 Rest of Western Europe

21 Chapter 21: Vendor Landscape

  • 21.1 1010data
  • 21.2 Accenture
  • 21.3 Actian Corporation
  • 21.4 Actuate Corporation
  • 21.5 Adaptive Insights
  • 21.6 Advizor Solutions
  • 21.7 AeroSpike
  • 21.8 AFS Technologies
  • 21.9 Alpine Data Labs
  • 21.10 Alteryx
  • 21.11 Altiscale
  • 21.12 Antivia
  • 21.13 Arcplan
  • 21.14 Attivio
  • 21.15 Automated Insights
  • 21.16 AWS (Amazon Web Services)
  • 21.17 Ayasdi
  • 21.18 Basho
  • 21.19 BeyondCore
  • 21.20 Birst
  • 21.21 Bitam
  • 21.22 Board International
  • 21.23 Booz Allen Hamilton
  • 21.24 Capgemini
  • 21.25 Cellwize
  • 21.26 Centrifuge Systems
  • 21.27 CenturyLink
  • 21.28 Chartio
  • 21.29 Cisco Systems
  • 21.30 ClearStory Data
  • 21.31 Cloudera
  • 21.32 Comptel
  • 21.33 Concurrent
  • 21.34 Contexti
  • 21.35 Couchbase
  • 21.36 CSC (Computer Science Corporation)
  • 21.37 DataHero
  • 21.38 Datameer
  • 21.39 DataRPM
  • 21.40 DataStax
  • 21.41 Datawatch Corporation
  • 21.42 DDN (DataDirect Network)
  • 21.43 Decisyon
  • 21.44 Dell
  • 21.45 Deloitte
  • 21.46 Denodo Technologies
  • 21.47 Digital Reasoning
  • 21.48 Dimensional Insight
  • 21.49 Domo
  • 21.50 Dundas Data Visualization
  • 21.51 Eligotech
  • 21.52 EMC Corporation
  • 21.53 Engineering Group (Engineering Ingegneria Informatica)
  • 21.54 eQ Technologic
  • 21.55 Facebook
  • 21.56 FICO
  • 21.57 Fractal Analytics
  • 21.58 Fujitsu
  • 21.59 Fusion-io
  • 21.60 GE (General Electric)
  • 21.61 GoodData Corporation
  • 21.62 Google
  • 21.63 Guavus
  • 21.64 HDS (Hitachi Data Systems)
  • 21.65 Hortonworks
  • 21.66 HPE (Hewlett Packard Enterprise)
  • 21.67 IBM
  • 21.68 iDashboards
  • 21.69 Incorta
  • 21.70 InetSoft Technology Corporation
  • 21.71 InfiniDB
  • 21.72 Infor
  • 21.73 Informatica Corporation
  • 21.74 Information Builders
  • 21.75 Intel
  • 21.76 Jedox
  • 21.77 Jinfonet Software
  • 21.78 Juniper Networks
  • 21.79 Knime
  • 21.80 Kofax
  • 21.81 Kognitio
  • 21.82 L-3 Communications
  • 21.83 Lavastorm Analytics
  • 21.84 Logi Analytics
  • 21.85 Looker Data Sciences
  • 21.86 LucidWorks
  • 21.87 Maana
  • 21.88 Manthan Software Services
  • 21.89 MapR
  • 21.90 MarkLogic
  • 21.91 MemSQL
  • 21.92 Microsoft
  • 21.93 MicroStrategy
  • 21.94 MongoDB (formerly 10gen)
  • 21.95 Mu Sigma
  • 21.96 NTT Data
  • 21.97 Neo Technology
  • 21.98 NetApp
  • 21.99 Nutonian
  • 21.100 OpenText Corporation
  • 21.101 Opera Solutions
  • 21.102 Oracle
  • 21.103 Palantir Technologies
  • 21.104 Panorama Software
  • 21.105 ParStream
  • 21.106 Pentaho
  • 21.107 Phocas
  • 21.108 Pivotal Software
  • 21.109 Platfora
  • 21.110 Prognoz
  • 21.111 PwC
  • 21.112 Pyramid Analytics
  • 21.113 Qlik
  • 21.114 Quantum Corporation
  • 21.115 Qubole
  • 21.116 Rackspace
  • 21.117 RapidMiner
  • 21.118 Recorded Future
  • 21.119 RJMetrics
  • 21.120 Salesforce.com
  • 21.121 Sailthru
  • 21.122 Salient Management Company
  • 21.123 SAP
  • 21.124 SAS Institute
  • 21.125 SGI
  • 21.126 SiSense
  • 21.127 Software AG
  • 21.128 Splice Machine
  • 21.129 Splunk
  • 21.130 Sqrrl
  • 21.131 Strategy Companion
  • 21.132 Supermicro
  • 21.133 Syncsort
  • 21.134 SynerScope
  • 21.135 Tableau Software
  • 21.136 Talend
  • 21.137 Targit
  • 21.138 TCS (Tata Consultancy Services)
  • 21.139 Teradata
  • 21.140 Think Big Analytics
  • 21.141 ThoughtSpot
  • 21.142 TIBCO Software
  • 21.143 Tidemark
  • 21.144 VMware (EMC Subsidiary)
  • 21.145 WiPro
  • 21.146 Yellowfin International
  • 21.147 Zendesk
  • 21.148 Zettics
  • 21.149 Zoomdata
  • 21.150 Zucchetti

22 Chapter 22: Conclusion & Strategic Recommendations

  • 22.1 Big Data Technology: Beyond Data Capture & Analytics
  • 22.2 Transforming IT from a Cost Center to a Profit Center
  • 22.3 Can Privacy Implications Hinder Success?
  • 22.4 Will Regulation have a Negative Impact on Big Data Investments?
  • 22.5 Battling Organization & Data Silos
  • 22.6 Software vs. Hardware Investments
  • 22.7 Vendor Share: Who Leads the Market?
  • 22.8 Big Data Driving Wider IT Industry Investments
  • 22.9 Assessing the Impact of IoT & M2M
  • 22.10 Recommendations
    • 22.10.1 Big Data Hardware, Software & Professional Services Providers
    • 22.10.2 Enterprises

List of Figures

  • Figure 1: Reactive vs. Proactive Analytics
  • Figure 2: Big Data Industry Roadmap
  • Figure 3: The Big Data Value Chain
  • Figure 4: Global Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 5: Global Big Data Revenue by Submarket: 2016 - 2030 ($ Million)
  • Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2016 - 2030 ($ Million)
  • Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2016 - 2030 ($ Million)
  • Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2016 - 2030 ($ Million)
  • Figure 9: Global Big Data SQL Submarket Revenue: 2016 - 2030 ($ Million)
  • Figure 10: Global Big Data NoSQL Submarket Revenue: 2016 - 2030 ($ Million)
  • Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2016 - 2030 ($ Million)
  • Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2016 - 2030 ($ Million)
  • Figure 13: Global Big Data Professional Services Submarket Revenue: 2016 - 2030 ($ Million)
  • Figure 14: Global Big Data Revenue by Vertical Market: 2016 - 2030 ($ Million)
  • Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2016 - 2030 ($ Million)
  • Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2016 - 2030 ($ Million)
  • Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2016 - 2030 ($ Million)
  • Figure 18: Global Big Data Revenue in the Education Sector: 2016 - 2030 ($ Million)
  • Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2016 - 2030 ($ Million)
  • Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2016 - 2030 ($ Million)
  • Figure 21: Global Big Data Revenue in the Insurance Sector: 2016 - 2030 ($ Million)
  • Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2016 - 2030 ($ Million)
  • Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2016 - 2030 ($ Million)
  • Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2016 - 2030 ($ Million)
  • Figure 25: Global Big Data Revenue in the Public Services Sector: 2016 - 2030 ($ Million)
  • Figure 26: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2016 - 2030 ($ Million)
  • Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2016 - 2030 ($ Million)
  • Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2016 - 2030 ($ Million)
  • Figure 29: Global Big Data Revenue in Other Vertical Sectors: 2016 - 2030 ($ Million)
  • Figure 30: Big Data Revenue by Region: 2016 - 2030 ($ Million)
  • Figure 31: Asia Pacific Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 32: Asia Pacific Big Data Revenue by Country: 2016 - 2030 ($ Million)
  • Figure 33: Australia Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 34: China Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 35: India Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 36: Indonesia Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 37: Japan Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 38: Malaysia Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 39: Pakistan Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 40: Philippines Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 41: Singapore Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 42: South Korea Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 43: Taiwan Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 44: Thailand Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 45: Big Data Revenue in the Rest of Asia Pacific: 2016 - 2030 ($ Million)
  • Figure 46: Eastern Europe Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 47: Eastern Europe Big Data Revenue by Country: 2016 - 2030 ($ Million)
  • Figure 48: Czech Republic Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 49: Poland Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 50: Russia Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 51: Big Data Revenue in the Rest of Eastern Europe: 2016 - 2030 ($ Million)
  • Figure 52: Latin & Central America Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 53: Latin & Central America Big Data Revenue by Country: 2016 - 2030 ($ Million)
  • Figure 54: Argentina Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 55: Brazil Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 56: Mexico Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 57: Big Data Revenue in the Rest of Latin & Central America: 2016 - 2030 ($ Million)
  • Figure 58: Middle East & Africa Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 59: Middle East & Africa Big Data Revenue by Country: 2016 - 2030 ($ Million)
  • Figure 60: Israel Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 61: Qatar Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 62: Saudi Arabia Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 63: South Africa Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 64: UAE Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 65: Big Data Revenue in the Rest of the Middle East & Africa: 2016 - 2030 ($ Million)
  • Figure 66: North America Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 67: North America Big Data Revenue by Country: 2016 - 2030 ($ Million)
  • Figure 68: Canada Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 69: USA Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 70: Western Europe Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 71: Western Europe Big Data Revenue by Country: 2016 - 2030 ($ Million)
  • Figure 72: Denmark Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 73: Finland Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 74: France Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 75: Germany Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 76: Italy Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 77: Netherlands Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 78: Norway Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 79: Spain Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 80: Sweden Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 81: UK Big Data Revenue: 2016 - 2030 ($ Million)
  • Figure 82: Big Data Revenue in the Rest of Western Europe: 2016 - 2030 ($ Million)
  • Figure 83: Global Big Data Revenue by Hardware, Software & Professional Services: 2016 - 2030 ($ Million)
  • Figure 84: Big Data Vendor Market Share (%)
  • Figure 85: Global IT Expenditure Driven by Big Data Investments: 2016 - 2030 ($ Million)
  • Figure 86: Global M2M Connections by Access Technology: 2016 - 2030 (Millions)
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