【レポートの概要（一部）】
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: Vertical Opportunities & Use Cases for Big Data
3.1 Automotive, Aerospace & Transportation
3.1.1 Predictive Warranty Analysis
3.1.2 Predictive Aircraft Maintenance & Fuel Optimization
3.1.3 Air Traffic Control
3.1.4 Transport Fleet Optimization
3.2 Banking & Securities
3.2.1 Customer Retention & Personalized Product Offering
3.2.2 Risk Management
3.2.3 Fraud Detection
3.2.4 Credit Scoring
3.3 Defense & Intelligence
3.3.1 Intelligence Gathering
3.3.2 Energy Saving Opportunities in the Battlefield
3.3.3 Preventing Injuries on the Battlefield
3.4 Education
3.4.1 Information Integration
3.4.2 Identifying Learning Patterns
3.4.3 Enabling Student-Directed Learning
3.5 Healthcare & Pharmaceutical
3.5.1 Managing Population Health Efficiently
3.5.2 Improving Patient Care with Medical Data Analytics
3.5.3 Improving Clinical Development & Trials
3.5.4 Improving Time to Market
3.6 Smart Cities & Intelligent Buildings
3.6.1 Energy Optimization & Fault Detection
3.6.2 Intelligent Building Analytics
3.6.3 Urban Transportation Management
3.6.4 Optimizing Energy Production
3.6.5 Water Management
3.6.6 Urban Waste Management
3.7 Insurance
3.7.1 Claims Fraud Mitigation
3.7.2 Customer Retention & Profiling
3.7.3 Risk Management
3.8 Manufacturing & Natural Resources
3.8.1 Asset Maintenance & Downtime Reduction
3.8.2 Quality & Environmental Impact Control
3.8.3 Optimized Supply Chain
3.8.4 Exploration & Identification of Wells & Mines
3.8.5 Maximizing the Potential of Drilling
3.8.6 Production Optimization
3.9 Web, Media & Entertainment
3.9.1 Audience & Advertising Optimization
3.9.2 Channel Optimization
3.9.3 Recommendation Engines
3.9.4 Optimized Search
3.9.5 Live Sports Event Analytics
3.9.6 Outsourcing Big Data Analytics to Other Verticals
3.10 Public Safety & Homeland Security
3.10.1 Cyber Crime Mitigation
3.10.2 Crime Prediction Analytics
3.10.3 Video Analytics & Situational Awareness
3.11 Public Services
3.11.1 Public Sentiment Analysis
3.11.2 Fraud Detection & Prevention
3.11.3 Economic Analysis
3.12 Retail & Hospitality
3.12.1 Customer Sentiment Analysis
3.12.2 Customer & Branch Segmentation
3.12.3 Price Optimization
3.12.4 Personalized Marketing
3.12.5 Optimized Supply Chain
3.13 Telecommunications
3.13.1 Network Performance & Coverage Optimization
3.13.2 Customer Churn Prevention
3.13.3 Personalized Marketing
3.13.4 Location Based Services
3.13.5 Fraud Detection
3.14 Utilities & Energy
3.14.1 Customer Retention
3.14.2 Forecasting Energy
3.14.3 Billing Analytics
3.14.4 Predictive Maintenance
3.14.5 Turbine Placement Optimization
3.15 Wholesale Trade
3.15.1 In-field Sales Analytics
3.15.2 Monitoring the Supply Chain
4 Chapter 4: Big Data Industry Roadmap & Value Chain
4.1 Big Data Industry Roadmap
4.1.1 2010 – 2013: Initial Hype and the Rise of Analytics
4.1.2 2014 – 2017: Emergence of SaaS Based Big Data Solutions
4.1.3 2018 – 2020: Growing Adoption of Scalable Machine Learning
4.1.4 2021 & Beyond: Widespread Investments on Cognitive & Personalized Analytics
4.2 The Big Data Value Chain
4.2.1 Hardware Providers
4.2.1.1 Storage & Compute Infrastructure Providers
4.2.1.2 Networking Infrastructure Providers
4.2.2 Software Providers
4.2.2.1 Hadoop & Infrastructure Software Providers
4.2.2.2 SQL & NoSQL Providers
4.2.2.3 Analytic Platform & Application Software Providers
4.2.2.4 Cloud Platform Providers
4.2.3 Professional Services Providers
4.2.4 End-to-End Solution Providers
4.2.5 Vertical Enterprises
5 Chapter 5: Big Data Analytics
5.1 What are Big Data Analytics?
5.2 The Importance of Analytics
5.3 Reactive vs. Proactive Analytics
5.4 Customer vs. Operational Analytics
5.5 Technology & Implementation Approaches
5.5.1 Grid Computing
5.5.2 In-Database Processing
5.5.3 In-Memory Analytics
5.5.4 Machine Learning & Data Mining
5.5.5 Predictive Analytics
5.5.6 NLP (Natural Language Processing)
5.5.7 Text Analytics
5.5.8 Visual Analytics
5.5.9 Social Media, IT & Telco Network Analytics
5.6 Vertical Market Case Studies
5.6.1 Amazon – Delivering Cloud Based Big Data Analytics
5.6.2 Facebook – Using Analytics to Monetize Users with Advertising
5.6.3 WIND Mobile – Using Analytics to Monitor Video Quality
5.6.4 Coriant Analytics Services – SaaS Based Big Data Analytics for Telcos
5.6.5 Boeing – Analytics for the Battlefield
5.6.6 The Walt Disney Company – Utilizing Big Data and Analytics in Theme Parks
6 Chapter 6: Standardization & Regulatory Initiatives
6.1 CSCC (Cloud Standards Customer Council) – Big Data Working Group
6.2 NIST (National Institute of Standards and Technology) – Big Data Working Group
6.3 OASIS –Technical Committees
6.4 ODaF (Open Data Foundation)
6.5 Open Data Center Alliance
6.6 CSA (Cloud Security Alliance) – Big Data Working Group
6.7 ITU (International Telecommunications Union)
6.8 ISO (International Organization for Standardization) and Others
7 Chapter 7: Market Analysis & Forecasts
7.1 Global Outlook of the Big Data Market
7.2 Submarket Segmentation
7.2.1 Storage and Compute Infrastructure
7.2.2 Networking Infrastructure
7.2.3 Hadoop & Infrastructure Software
7.2.4 SQL
7.2.5 NoSQL
7.2.6 Analytic Platforms & Applications
7.2.7 Cloud Platforms
7.2.8 Professional Services
7.3 Vertical Market Segmentation
7.3.1 Automotive, Aerospace & Transportation
7.3.2 Banking & Securities
7.3.3 Defense & Intelligence
7.3.4 Education
7.3.5 Healthcare & Pharmaceutical
7.3.6 Smart Cities & Intelligent Buildings
7.3.7 Insurance
7.3.8 Manufacturing & Natural Resources
7.3.9 Media & Entertainment
7.3.10 Public Safety & Homeland Security
7.3.11 Public Services
7.3.12 Retail & Hospitality
7.3.13 Telecommunications
7.3.14 Utilities & Energy
7.3.15 Wholesale Trade
7.3.16 Other Sectors
7.4 Regional Outlook
7.5 Asia Pacific
7.5.1 Country Level Segmentation
7.5.2 Australia
7.5.3 China
7.5.4 India
7.5.5 Indonesia
7.5.6 Japan
7.5.7 Malaysia
7.5.8 Pakistan
7.5.9 Philippines
7.5.10 Singapore
7.5.11 South Korea
7.5.12 Taiwan
7.5.13 Thailand
7.5.14 Rest of Asia Pacific
7.6 Eastern Europe
7.6.1 Country Level Segmentation
7.6.2 Czech Republic
7.6.3 Poland
7.6.4 Russia
7.6.5 Rest of Eastern Europe
7.7 Latin & Central America
7.7.1 Country Level Segmentation
7.7.2 Argentina
7.7.3 Brazil
7.7.4 Mexico
7.7.5 Rest of Latin & Central America
7.8 Middle East & Africa
7.8.1 Country Level Segmentation
7.8.2 Israel
7.8.3 Qatar
7.8.4 Saudi Arabia
7.8.5 South Africa
7.8.6 UAE
7.8.7 Rest of the Middle East & Africa
7.9 North America
7.9.1 Country Level Segmentation
7.9.2 Canada
7.9.3 USA
7.10 Western Europe
7.10.1 Country Level Segmentation
7.10.2 Denmark
7.10.3 Finland
7.10.4 France
7.10.5 Germany
7.10.6 Italy
7.10.7 Netherlands
7.10.8 Norway
7.10.9 Spain
7.10.10 Sweden
7.10.11 UK
7.10.12 Rest of Western Europe
8 Chapter 8: Vendor Landscape
8.1 1010data
8.2 Accenture
8.3 Actian Corporation
8.4 Actuate Corporation
8.5 Adaptive Insights
8.6 Advizor Solutions
8.7 AeroSpike
8.8 AFS Technologies
8.9 Alpine Data Labs
8.10 Alteryx
8.11 Altiscale
8.12 Antivia
8.13 Arcplan
8.14 Attivio
8.15 Automated Insights
8.16 AWS (Amazon Web Services)
8.17 Ayasdi
8.18 Basho
8.19 BeyondCore
8.20 Birst
8.21 Bitam
8.22 Board International
8.23 Booz Allen Hamilton
8.24 Capgemini
8.25 Cellwize
8.26 Centrifuge Systems
8.27 CenturyLink
8.28 Chartio
8.29 Cisco Systems
8.30 ClearStory Data
8.31 Cloudera
8.32 Comptel
8.33 Concurrent
8.34 Contexti
8.35 Couchbase
8.36 CSC (Computer Science Corporation)
8.37 DataHero
8.38 Datameer
8.39 DataRPM
8.40 DataStax
8.41 Datawatch Corporation
8.42 DDN (DataDirect Network)
8.43 Decisyon
8.44 Dell
8.45 Deloitte
8.46 Denodo Technologies
8.47 Digital Reasoning
8.48 Dimensional Insight
8.49 Domo
8.50 Dundas Data Visualization
8.51 Eligotech
8.52 EMC Corporation
8.53 Engineering Group (Engineering Ingegneria Informatica)
8.54 eQ Technologic
8.55 Facebook
8.56 FICO
8.57 Fractal Analytics
8.58 Fujitsu
8.59 Fusion-io
8.60 GE (General Electric)
8.61 GoodData Corporation
8.62 Google
8.63 Guavus
8.64 HDS (Hitachi Data Systems)
8.65 Hortonworks
8.66 HP
8.67 IBM
8.68 iDashboards
8.69 Incorta
8.70 InetSoft Technology Corporation
8.71 InfiniDB
8.72 Infor
8.73 Informatica Corporation
8.74 Information Builders
8.75 Intel
8.76 Jedox
8.77 Jinfonet Software
8.78 Juniper Networks
8.79 Knime
8.80 Kofax
8.81 Kognitio
8.82 L-3 Communications
8.83 Lavastorm Analytics
8.84 Logi Analytics
8.85 Looker Data Sciences
8.86 LucidWorks
8.87 Manthan Software Services
8.88 MapR
8.89 MarkLogic
8.90 MemSQL
8.91 Microsoft
8.92 MicroStrategy
8.93 MongoDB (formerly 10gen)
8.94 Mu Sigma
8.95 NTT Data
8.96 Neo Technology
8.97 NetApp
8.98 OpenText Corporation
8.99 Opera Solutions
8.100 Oracle
8.101 Palantir Technologies
8.102 Panorama Software
8.103 ParStream
8.104 Pentaho
8.105 Phocas
8.106 Pivotal Software
8.107 Platfora
8.108 Prognoz
8.109 PwC
8.110 Pyramid Analytics
8.111 Qlik
8.112 Quantum Corporation
8.113 Qubole
8.114 Rackspace
8.115 RainStor
8.116 RapidMiner
8.117 Recorded Future
8.118 Revolution Analytics
8.119 RJMetrics
8.120 Salesforce.com
8.121 Sailthru
8.122 Salient Management Company
8.123 SAP
8.124 SAS Institute
8.125 SGI
8.126 SiSense
8.127 Software AG
8.128 Splice Machine
8.129 Splunk
8.130 Sqrrl
8.131 Strategy Companion
8.132 Supermicro
8.133 SynerScope
8.134 Tableau Software
8.135 Talend
8.136 Targit
8.137 TCS (Tata Consultancy Services)
8.138 Teradata
8.139 Think Big Analytics
8.140 ThoughtSpot
8.141 TIBCO Software
8.142 Tidemark
8.143 VMware (EMC Subsidiary)
8.144 WiPro
8.145 Yellowfin International
8.146 Zettics
8.147 Zoomdata
8.148 Zucchetti
9 Chapter 9: Conclusion & Strategic Recommendations
9.1 Big Data Technology: Beyond Data Capture & Analytics
9.2 Transforming IT from a Cost Center to a Profit Center
9.3 Can Privacy Implications Hinder Success?
9.4 Will Regulation have a Negative Impact on Big Data Investments?
9.5 Battling Organization & Data Silos
9.6 Software vs. Hardware Investments
9.7 Vendor Share: Who Leads the Market?
9.8 Big Data Driving Wider IT Industry Investments
9.9 Assessing the Impact of IoT & M2M
9.10 Recommendations
9.10.1 Big Data Hardware, Software & Professional Services Providers
9.10.2 Enterprises
【レポート販売概要】
■ タイトル：世界のビックデータ市場：機会、課題、戦略、産業、予測■ 英文：The Big Data Market: 2015 – 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts
■ 発行日：2015年5月25日
■ 調査会社：Signals and Systems Telecom
■ 商品コード：SNST50602
■ 調査対象地域：グローバル
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