株式会社グローバルインフォメーション
TEL: 044-952-0102
表紙
市場調査レポート

世界におけるワイヤレスキャリアの市場機会:スマートシティ・ホーム・ソリューション

Market Opportunities for Global Wireless Carriers in Smart Cities, Homes, and Solutions

発行 Mind Commerce 商品コード 323512
出版日 ページ情報 英文 349 Pages
納期: 即日から翌営業日
価格
本日の銀行送金レート: 1USD=114.77円で換算しております。
Back to Top
世界におけるワイヤレスキャリアの市場機会:スマートシティ・ホーム・ソリューション Market Opportunities for Global Wireless Carriers in Smart Cities, Homes, and Solutions
出版日: 2015年01月29日 ページ情報: 英文 349 Pages
概要

2019年までにキャリア総収益の最大15%が、スマートシティに依存すると予測されています。

当レポートでは、世界のワイヤレスキャリアにとってのLTE-Advanced、M2M、IoT、コネクテッドデバイス、ビッグデータおよび分析などの分野におけるスマートシティ、ホーム、およびソリューションの機会について調査しており、スマートシティ企業・ソリューション、地域別のスマートシティ投資・計画・プロジェクト、および市場展望と予測などについてまとめ、お届け致します。

第1章 エグゼクティブサマリー

第2章 イントロダクション

第3章 スマートシティ計画

  • 都市開発
  • ユーティリティとスマートグリッド
  • 通信インフラ

第4章 スマートシティ企業・ソリューション

  • ABB
  • ACCENTURE
  • ALCATEL LUCENT
  • CISCO SYSTEMS
  • CUBIC
  • HONEYWELL
  • IBM
  • INTEL
  • ORACLE
  • SIEMENS AG

第5章 産業におけるスマートシティの影響

  • 通信とスマートホーム
  • エネルギー管理
  • 産業オートメーション
  • 輸送
  • セキュリティ

第6章 世界のスマートシティ投資・計画・予測

  • アジア太平洋地域
  • 欧州
  • 北米
  • 南米

第7章 市場展望・予測

  • 世界的な大都市の成長・スマートシティ投資
  • スマートホーム収益
  • コネクテッド消費者デバイス
  • スマートシティにおけるモノのインターネット(IoT)の予測

第8章 結論・提言

  • エコシステムの影響
  • 市民の関与および技術協力の必要性
  • 有権者の協力
  • 最終結論

図表

目次

Smart Cities are much more than just an effort by sovereign nations to modernize their infrastructure - they are a focal point for growth drivers in several key ICT areas including: M2M/IoT, Connected Devices, Broadband Wireless, Cloud Computing, Big Data and Analytics. Smart City developments are causing many technologies and solutions to integrate with convergence seen across with many resource areas including energy, water, sanitation, and other essential services.

Mind Commerce sees significant opportunities for global wireless carriers in Smart Cities, Homes, and Solutions in the areas of LTE-Advanced, M2M, IoT, Connected Devices, Big Data and Analytics as well as a vast number of applications. The importance of wireless carrier investment in Smart Cities and Homes cannot be understated. By way of example, our research indicates that a significant majority of IoT applications will occur within metropolitan areas and will ultimately integrate within a Smart City ecosystem. Our research indicates up to 15% of all carrier revenue will be dependent upon Smart Cities by 2019.

This research offering includes comprehensive analysis in all key areas for global wireless carriers including:

  • Smart City and Homes
  • LTE Advanced (LTE-A)
  • Big Data and Analytics
  • M2M Internet of Things
  • M2M/IoT Smart City Apps

Report Benefits:

  • Smart City and Smart Home forecasts
  • Market data for LTE-A, M2M, IoT, Big Data and Analytics
  • Identify Market opportunities for carriers in Smart Cities/Homes
  • Identify the market drivers for Smart Cities and Homes and impact on ICT
  • Understand the impact of Smart Cities/Homes on telecom services evolution
  • Understand the technologies and investment areas for supporting Smart Cities/Homes

All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Table of Contents

Smart Cities: Global Outlook and Forecasts

1.0. EXECUTIVE SUMMARY

2.0. INTRODUCTION

  • 2.1. WHAT IS SMART CITY
  • 2.2. MARKET DRIVERS FOR SMART CITIES
  • 2.3. SMART CITY SUPPORTING TECHNOLOGIES

3.0. SMART CITY PLANNING

  • 3.1. URBAN DEVELOPMENT
  • 3.2. UTILITIES AND SMART GRIDS
  • 3.3. TELECOM INFRASTRUCTURE

4.0. SMART CITIES COMPANIES AND SOLUTIONS

  • 4.1. ABB
  • 4.2. ACCENTURE
  • 4.3. ALCATEL LUCENT
  • 4.4. CISCO SYSTEMS
  • 4.5. CUBIC
  • 4.6. HONEYWELL
  • 4.7. IBM
  • 4.8. INTEL
  • 4.9. ORACLE
  • 4.10. SIEMENS AG

5.0. SMART CITY IMPACT ON INDUSTRY VERTICALS

  • 5.1. TELECOM AND SMART HOME
  • 5.2. ENERGY MANAGEMENT
  • 5.3. INDUSTRIAL AUTOMATION
  • 5.4. TRANSPORTATION
  • 5.5. SECURITY

6.0. GLOBAL SMART CITY INVESTMENT, PLANNING AND PROJECTS

  • 6.1. ASIA PACIFIC
  • 6.2. EUROPE
  • 6.3. NORTH AMERICA
  • 6.4. SOUTH AMERICA

7.0. MARKET OUTLOOK AND FORECASTING

  • 7.1. GLOBAL METROPOLITAN GROWTH AND SMART CITY INVESTMENTS
  • 7.2. SMART HOME REVENUE 2014 - 2019
  • 7.3. CONNECTED CONSUMER DEVICES 2014 - 2019
  • 7.4. FORECASTING THE INTERNET OF THINGS (IOT) IN THE SMART CITY IMPACT

8.0. CONCLUSIONS AND RECOMMENDATIONS

  • 8.1. ECOSYSTEM IMPACT
  • 8.2. NEED FOR CITIZEN ENGAGEMENT AND TECHNOLOGY COLLABORATION
  • 8.3. CONSTITUENT COLLABORATION
  • 8.4. FINAL CONCLUSIONS

Figures

  • Figure 1: Smart City Concept
  • Figure 2: Smart City Participants
  • Figure 3: HetNet Topology
  • Figure 4: WiMAX Communications
  • Figure 5: Smart City Infrastructure
  • Figure 6: ABB Smart City Offerings
  • Figure 7: Accenture Smart City Offerings
  • Figure 8: Trends and Smart City Future
  • Figure 9: Smart City Market Sizing
  • Figure 10: Smart City Investment Asia Pac 2014 - 2023

Tables

  • Table 1: Global Consumer Smart Home Products & Services Revenue 2014 - 2019
  • Table 2: Global Households, Broadband, and Smart Homes 2014 - 2019
  • Table 3: Global Connected Consumer Device Revenue by Type 2014 - 2019
  • Table 4: Global Internet of Things Objects 2014 - 2019

Smart Homes: Companies and Solutions 2

INTRODUCTION TO SMART HOMES

WHAT IS SMART HOME TECHNOLOGY?

SMART HOME ENVIRONMENT

  • Home Automation System
  • Home Automation Standards and Architectures
  • Centralized Architecture
  • Distributed Architecture
  • Mixed Architecture
  • Home Automation Network
  • Bus Standards
  • Open Standards or Proprietary Protocols and Procedures
  • European Installation Bus (EIB _
  • KNX
  • Local Operating Network (LON)
  • X10
  • BACnet
  • Internet protocol (IP)
  • Mediums for transfer of signals
  • Communication In-House and Out-of-House
  • Interface
  • Standard Units
  • Mobile Phone
  • Cordless DECT Telephones

BENEFITS OF HOME AUTOMATION

  • Convenience
  • Security
  • Savings
  • Value

SMART HOMES GLOBAL MARKET ASSESSMENT

  • Smart Homes Market Demand
  • Smart Home Market Growth Drivers
  • Always-Connected Customers
  • Energy Conservation
  • New Technologies
  • Market Segments and Potential
  • Smart Homes in the Developed Market
  • Demographics Analysis
  • Age
  • Education and Culture
  • Cost
  • Usability
  • Quality
  • Developing Market
  • Home Automation Usage and Purchase Factors
  • Cost
  • Quality
  • Usability
  • Warranty and Support
  • Emerging Market
  • Demographics Analysis
  • Age
  • Trust
  • Financially-Safe
  • Warranty and Support

HOME AUTOMATION APPLICATIONS

SMART LIGHTING

  • Smart Lighting Market Demand
  • Types of Lamps
  • Phased out Lamps
  • Conventional Incandescent Lamp (GLS)
  • Conventional Halogen Lamps
  • Available Alternatives
  • Conventional Low-voltage Halogen lamps
  • Halogen lamps with xenon gas filling (C-class)
  • Halogen Lamps with Infrared Coating (B-class)
  • Compact fluorescent lamps (CFLs)
  • Light-emitting Diodes (LEDs)
  • Techniques of Smart Lighting
  • Smart Lighting Control
  • Daylight Sensing
  • Occupancy Sensing
  • An Internet Address for Every Light Bulb

HOME ENTERTAINMENT

  • Home Entertainment Market Demand
  • Home Entertainment Applications
  • Home Theater
  • Whole House Audio
  • Video Distribution

SMART HOME SECURITY

  • Smart Home Security Market Demand
  • Smart Home Security Components
  • Doors and Windows Security
  • Motion Sensors
  • Security Alarm
  • Surveillance Cameras
  • Home Health Monitoring - Telehealth

SMART GRID AND SMART APPLIANCES

  • Smart Grid Market Demand

SMART HOMES FUTURE OUTLOOK

TOWARDS LESS PRICE AND HIGHER AWARENESS

BUILDING DIFFERENT CUSTOMERS BASE

DIY AUTOMATION

THE INTERNET OF THINGS: CONNECTING THE SMART HOME. 56

Figures

  • FIGURE 1: INSTALLED SMART HOMES US 2012 - 2017
  • FIGURE 2: MOBILE DEVICES PER USER 2014 - 2018
  • FIGURE 3: US RESIDENTIAL AND COMMERCIAL LIGHTING CONSUMPTION
  • FIGURE 4: ENERGY SAVINGS BY LAMP TECHNOLOGIES
  • FIGURE 5: SONY REVENUE BY SECTOR 2012 - 2014
  • FIGURE 6: HOME SECURITY MARKET GROWTH 2011 - 2017
  • FIGURE 7: MARKET VALUE FOR THE SMARTGRID COMMUNICATION NETWORKS IN US 2010 - 2015
  • FIGURE 8: PROJECTED GLOBAL SMART GRID INVESTMENT 2009 - 2015
  • FIGURE 9: GROWTH OF MOBILE DEVICES 2015 - 2020
  • FIGURE 10: CELLULAR CONNECTION GROWTH 2010 - 2020
  • FIGURE 11: ENERGY SMART HOME LAB

Machine-to-Machine Communications: What Executives and IT Leaders Need to Know about M2M and its Role in Support of IoT

1. Executive Summary

2. Introduction to M2M

  • 2.1. M2M Overview
  • 2.2. M2M Basics
    • 2.2.1. Data Acquisition
    • 2.2.2. Data Transmission
    • 2.2.3. Data Analysis
  • 2.3. M2M in Industry Sectors
    • 2.3.1. Smart Grid
    • 2.3.2. Water Meters
    • 2.3.3. Healthcare
    • 2.3.4. Smart Meters
    • 2.3.5. Smart Cities
    • 2.3.6. Retail
    • 2.3.7. Connected Building
    • 2.3.8. Connected People
    • 2.3.9. Connected Vehicles
    • 2.3.10. Connected Infrastructure
    • 2.3.11. Connected Industrial Processes
    • 2.3.12. Connected Money
    • 2.3.13. M2M and Big Data
  • 2.4. M2M Ecosystem
    • 2.4.1. End Device/Equipment
    • 2.4.2. Consumer/End-user
    • 2.4.3. End Device/Equipment
    • 2.4.4. Sensors
    • 2.4.5. Applications
    • 2.4.6. Middleware Platform
    • 2.4.7. Embedded Module
    • 2.4.8. Subscriber Identity Module (SIM)
  • 2.5. M2M and the Internet of Things (IoT)
  • 2.6. M2M Applications
    • 2.6.1. Fleet and Field Service Management
    • 2.6.2. Manufacturing
    • 2.6.3. Healthcare
    • 2.6.4. Automotive
    • 2.6.5. Supply Chain Management
    • 2.6.6. Retail Management
    • 2.6.7. Smart Homes and Buildings
    • 2.6.8. Security and Surveillance
    • 2.6.9. Usage Based Insurance

3. M2M Market Adoption and Barrier

  • 3.1. M2M Adoption
  • 3.2. M2M Barriers/Challenges
  • 3.3. Improving M2M

4. M2M Market Opportunities and Future Outlook

  • 4.1. Market Forecast
  • 4.2. M2M Market Predictions
    • 4.2.1. Big Data Aligned with M2M
    • 4.2.2. Standards Strengthen
    • 4.2.3. Open Hardware
    • 4.2.4. Open Interfaces
    • 4.2.5. More Innovation by Start-ups
    • 4.2.6. M2M based Consumer Electronics will Reach Consumers
    • 4.2.7. Connected cars in Spot-light
    • 4.2.8. Transport Management Extends
    • 4.2.9. New products for Insurance Industry
    • 4.2.10. More installations of Smart Meters
    • 4.2.11. Smart Cities Thrive
  • 4.3. Future M2M Applications

5 Recommended Further Reading

Figures

  • Figure 1: Basic Building Blocks of M2M
  • Figure 2: The M2M Ecosystem (A)
  • Figure 3: The M2M Ecosystem (B)
  • Figure 4: The M2M Ecosystem (C)
  • Figure 5: The M2M Ecosystem (D)
  • Figure 6: Cellular M2M Connections Forecast 2014 - 2020
  • Figure 7: Cellular M2M as a Percentage of Total Mobile Connections

Smart Home, Building, and City Machine-to-Machine (M2M) Applications

EXECUTIVE SUMMARY

1.0. SMART HOME

  • 2.1. Primary Elements of Smart Home
    • 2.1.1. Infrastructure
    • 2.1.2. Sensors
    • 2.1.3. Actuators
    • 2.1.4. Applications
    • 2.1.5. Hub
  • 2.2. Real-life applications and solutions available for Smart Home
  • 2.3. Smart Home Vision
  • 2.4. Requirement of Smart Home Services
    • 2.4.1. Affordability
    • 2.4.2. Usability
    • 2.4.3. Reliability
  • 2.5. Stages of Smart Home Services
    • 2.5.1. Stage 1 - Connected Standalone Devices
    • 2.5.2. Stage 2 - Connected Service Silos
    • 2.5.3. Stage 3 - Integrated Smart Home
  • 2.6. Smart Home Ecosystem Requirements
    • 2.6.1. Home Environment
    • 2.6.2. Wide Area Connectivity
    • 2.6.3. Back-end Environment
    • 2.6.4. Enabling Service Features
    • 2.6.5. Third Party Service Providers

2.0. SMART HOME SOLUTION FOR SUSTAINABLE HOMES

  • 3.1. Sustainability
  • 3.2. Smart Home parameters to support sustainable home concept
    • 3.2.1. Thermal Comfort
    • 3.2.2. Water
    • 3.2.3. Communications and Entertainment
    • 3.2.4. Safety & Security
    • 3.2.5. Lighting
    • 3.2.6. Heath & Wellbeing
    • 3.2.7. Smart Meter
    • 3.2.8. Protecting the Building fabric

3.0. SMART BUILDING

  • 4.1. Security Solution
  • 4.2. Facilities Control
  • 4.3. Standardization in Smart Home Arena

4.0. CASE STUDY - SMART HOME AND SMART BUILDING

  • 5.1. Case: Smart Home Solution for Art Collector
    • 5.1.1. The Challenge
    • 5.1.2. The Solution
    • 5.1.3. The Result
  • 5.2. Case: Total Home Control Solution
    • 5.2.1. The Challenge
    • 5.2.2. The Solution
    • 5.2.3. The Result
  • 5.3. Case: Sir Richard Branson's Caribbean Smart Home
    • 5.3.1. The Challenge
    • 5.3.2. The Solution
    • 5.3.3. The Result
  • 5.4. Case: Energy Management
    • 5.4.1. The Challenge
    • 5.4.2. The Solution
    • 5.4.3. The Result
  • 5.5. Case : Real-time monitoring of oil levels
    • 5.5.1. The Challenge
    • 5.5.2. The Solution
    • 5.5.3. The Result
    • 5.5.4. Author's Note
  • 5.6. Case: Professional Golfer's Smart Home
    • 5.6.1. The Challenge
    • 5.6.2. The Solution
    • 5.6.3. The Result
  • 5.7. Case : Monitor Structural parameters in Real time
    • 5.7.1. The Challenge
    • 5.7.2. The Solution
    • 5.7.3. The Result
    • 5.7.4. Author's Note

5.0. CONCEPTS OF SMART CITY

  • 5.8. Objective of Smart City
  • 5.9. Essential Elements of Smart City
  • 5.10. Initial Steps for Creating Smart Cities
  • 5.11. Framework for Smart City
  • 5.12. Features of Smart City
  • 5.13. Use of M2M for Smart City
  • 5.14. Development activities for Smart City in India
  • 5.15. Development activities for Smart City in China
  • 5.16. Development activities for Smart City in Spain
    • 5.16.1. Wireless network in Santander
  • 5.17. Development activities for Smart City in Brazil
  • 5.18. Development activities for Smart City across the Globe
  • 5.19. Standards (or lack of it) for Smart City
  • 5.20. Open-Source Platform for developing Applications

6.0. CASE STUDY - SMART CITY

  • 6.1. Case : Solution for Traffic Safety
    • 6.1.1. The Challenge
    • 6.1.2. The Solution
    • 6.1.3. The Result
    • 6.1.4. Author's Note
  • 6.2. Case : Solution for Parking Meters
    • 6.2.1. The Challenge
    • 6.2.2. The Solution
    • 6.2.3. The Result
    • 6.2.4. Author's Note
  • 6.3. Case : Solution for 'smart' public convenience system
    • 6.3.1. The Challenge
    • 6.3.2. The Solution
    • 6.3.3. The Result
    • 6.3.4. Author's Note
  • 6.4. Case : Solution for Waste Disposal
    • 6.4.1. The Challenge
    • 6.4.2. The Solution
    • 6.4.3. The Result
    • 6.4.4. Author's Note
  • 6.5. Case : Experimental Research Facility for Smart City
    • 6.5.1. The Challenge
    • 6.5.2. The Solution
    • 6.5.3. The Result
    • 6.5.4. Author's Note

7.0. CONCLUDING REMARKS

Figures

  • Figure 1: Primary Elements of Smart Home
  • Figure 2: Samsung Smart Home Service
  • Figure 3: Revolv
  • Figure 4: Device by Savant Systems
  • Figure 5: Archos
  • Figure 6: HAPIfork
  • Figure 7: Belkin WeMo Smart Slow Cooker
  • Figure 8: LeakSmart Water Valve
  • Figure 9: Sleep Number x12 Bed
  • Figure 10: Whirlpool Smart Dishwasher
  • Figure 11: Netatmo Connected Weather Station
  • Figure 12: Koubachi Wi-Fi Plant Sensor
  • Figure 13: Nest Thermostat
  • Figure 14: Canary's multi-sensor security hub
  • Figure 16: Staples Connect
  • Figure 17: Belkin WeMo Home Automation
  • Figure 18: Smart Home Vision
  • Figure 19: Requirement of Smart Home Services
  • Figure 20: Stages of Smart Home Services
  • Figure 21: Stage 1: Connected Standalone Devices
  • Figure 22: Stage 2: Connected Service Silos
  • Figure 23: Stage 3: Integrated Smart Home
  • Figure 24: Smart Home Ecosystem Requirements
  • Figure 25: Smart Home parameters to support sustainable home concept
  • Figure 26: Intelligent Building
  • Figure 27: Objectives of Smart City
  • Figure 28: Essential elements of Smart City
  • Figure 29: Initial Steps for Creating Smart Cities
  • Figure 30: Important Tasks for Smart City
  • Figure 31: Smart City Framework
  • Figure 32: Features of Smart City

LTE Advanced: State of the Market and Future Prospects

  • Executive Summary
  • Background
  • Overview of Mobile Broadband
  • Overview
  • First Generation (1G)
  • 1G Features
  • Second Generation (2G)
  • 2G Features
  • 2.5G Wireless System
  • 2.75G (EDGE) Wireless System
  • 2.75G Features
  • Third Generation (3G)
  • Fourth Generation (4G)
  • 4G Features
  • Fifth Generation (5G)
  • 5G Features
  • Long Term Evolution (LTE)
  • LTE Advanced
  • Overview
  • Major LTE-Advanced Features
  • Carrier Aggregation (CA)
  • Enhanced Uplink Multiple Access and Enhanced Multiple Antenna Transmission
  • Coordinated Multipoint Transmission and Reception (CoMP)
  • Home Enhanced-node-B (HeNB) Mobility Enhancements (HetNet)
  • Competitive Analysis (LTE-Advanced vs. WiMAX 2)
  • Network Nature
  • Using OFDMA
  • Adaptive Modulation and Coding
  • Conclusion
  • LTE-Advanced Market Drivers
  • Key Enabler for Growth
  • Increased Adoption of Mobile Broadband
  • Speed and Cost
  • Hardware
  • Major LTE-Advanced Players
  • LTE-Advanced Demonstration
  • LTE-Advanced Demonstrations Distribution by Country
  • LTE-Advanced Deployments (Active, Planned)
  • LTE-Advanced Deployments Distribution by Country
  • Future Outlook and Forecasts
  • More Capacity will be followed by Great Demand
  • Fifth Generation (5G)
  • Appendix
  • LTE Infrastructure Elements and Architecture
  • LTE E-UTRAN
  • LTE Remote Radio Heads
  • LTE Base Station
  • LTE Femtocells
  • LTE Antenna Schemes
  • LTE RAN Infrastructure and Frequency Reuse
  • LTE EPC Infrastructure Elements
  • Serving and Packet Gateway
  • Mobility Management Entity
  • Policy and Charging Rules Function
  • IP Multimedia Subsystem
  • EPC and Core Network Equipment Reuse in LTE
  • LTE Architecture Details
  • Service Architecture
  • Layer 2 of LTE
  • Downlink Logical
  • Uplink Logical
  • Mobility Across eNBs

Figures

  • Figure 1: 1G Mobile Phone
  • Figure 2: 2G Specifications
  • Figure 3: 2G Mobile Phone
  • Figure 4: 3G Specifications
  • Figure 5: 3G Mobile Phone
  • Figure 6: 4G Mobile Phone
  • Figure 7: Release of 3GPP Specification
  • Figure 8: Key Radio Access Targets for LTE-Advanced as set by 3GPP
  • Figure 9: Upgrade from LTE to LTE-Advanced
  • Figure 10: Wireless Technology Evolution
  • Figure 11: Comparing Wireless Technologies based on Speed
  • Figure 12: Top Application Growth
  • Figure 13: Traffic Growth
  • Figure 14: End-users use WiFi Service when Available
  • Figure 15: LTE-Advanced Demonstrations and Trials by Country 2014
  • Figure 16: LTE Advanced Deployments by Country
  • Figure 17: LTE Advanced Deployments Target Speed (Maximum DL) (Mbps)
  • Figure 18: Mobile Traffic Forecast 2010 - 2020
  • Figure 19: LTE E-UTRAN Infrastructure Network Elements
  • Figure 20: LTE EPC Infrastructure Network Elements
  • Figure 21: Understanding LTE Network Elements and Channels

Fundamentals of Big Data, Predictive Analysis, and Business Intelligence

INTRODUCTION

TECHNICAL OVERVIEW

BIG DATA OVERVIEW

TECHNOLOGY TRENDS

MARKET OVERVIEW

MARKET FORECAST

MARKET ANALYSIS

MARKET PREDICTIONS

MARKET SECTORS

  • Science/Research
  • Government
  • Private Sector
  • Finance

BUSINESS OVERVIEW

BIG DATA TRANSITION CHALLENGES

RISKS AND ISSUES

SUMMARY

APPENDIX

The Big Data & Telco Analytics Market: Business Case, Market Analysis & Forecasts 2014 - 2

Chapter 1: Introduction

  • 1.1. Executive Summary
  • 1.2. Topics Covered
  • 1.3. Key Findings
  • 1.4. Target Audience
  • 1.5. Companies Mentioned

Chapter 2: Big Data Technology & Business Case

  • 2.1. Defining Big Data
  • 2.2. Key Characteristics of Big Data
    • 2.2.1. Volume
    • 2.2.2. Variety
    • 2.2.3. Velocity
    • 2.2.4. Variability
    • 2.2.5. Complexity
  • 2.3. Big Data Technology
    • 2.3.1. Hadoop
      • 2.3.1.1. MapReduce
      • 2.3.1.2. HDFS
      • 2.3.1.3. Other Apache Projects
    • 2.3.2. NoSQL
      • 2.3.2.1. Hbase
      • 2.3.2.2. Cassandra
      • 2.3.2.3. Mongo DB
      • 2.3.2.4. Riak
      • 2.3.2.5. CouchDB
    • 2.3.3. MPP Databases
    • 2.3.4. Others and Emerging Technologies
      • 2.3.4.1. Storm
      • 2.3.4.2. Drill
      • 2.3.4.3. Dremel
      • 2.3.4.4. SAP HANA
      • 2.3.4.5. Gremlin & Giraph
  • 2.4. Market Drivers
    • 2.4.1. Data Volume & Variety
    • 2.4.2. Increasing Adoption of Big Data by Enterprises & Telcos
    • 2.4.3. Maturation of Big Data Software
    • 2.4.4. Continued Investments in Big Data by Web Giants
  • 2.5. Market Barriers
    • 2.5.1. Privacy & Security: The 'Big' Barrier
    • 2.5.2. Workforce Re-skilling & Organizational Resistance
    • 2.5.3. Lack of Clear Big Data Strategies
    • 2.5.4. Technical Challenges: Scalability & Maintenance

Chapter 3: Key Investment Sectors for Big Data

  • 3.1. Industrial Internet & M2M
    • 3.1.1. Big Data in M2M
    • 3.1.2. Vertical Opportunities
  • 3.2. Retail & Hospitality
    • 3.2.1. Improving Accuracy of Forecasts & Stock Management
    • 3.2.2. Determining Buying Patterns
    • 3.2.3. Hospitality Use Cases
  • 3.3. Media
    • 3.3.1. Social Media
    • 3.3.2. Social Gaming Analytics
    • 3.3.3. Usage of Social Media Analytics by Other Verticals
  • 3.4. Utilities
    • 3.4.1. Analysis of Operational Data
    • 3.4.2. Application Areas for the Future
  • 3.5. Financial Services
    • 3.5.1. Fraud Analysis & Risk Profiling
    • 3.5.2. Merchant-Funded Reward Programs
    • 3.5.3. Customer Segmentation
    • 3.5.4. Insurance Companies
  • 3.6. Healthcare & Pharmaceutical
    • 3.6.1. Drug Development
    • 3.6.2. Medical Data Analytics
    • 3.6.3. Case Study: Identifying Heartbeat Patterns
  • 3.7. Telcos
    • 3.7.1. Telco Analytics: Customer/Usage Profiling and Service Optimization
    • 3.7.2. Speech Analytics
    • 3.7.3. Other Use Cases
  • 3.8. Government & Homeland Security
    • 3.8.1. Developing New Applications for the Public
    • 3.8.2. Tracking Crime
    • 3.8.3. Intelligence Gathering
    • 3.8.4. Fraud Detection & Revenue Generation
  • 3.9. Other Sectors
    • 3.9.1. Aviation: Air Traffic Control
    • 3.9.2. Transportation & Logistics: Optimizing Fleet Usage
    • 3.9.3. Sports: Real-Time Processing of Statistics

Chapter 4: The Big Data Value Chain

  • 4.1. How Fragmented is the Big Data Value Chain?
  • 4.2. Data Acquisitioning & Provisioning
  • 4.3. Data Warehousing & Business Intelligence
  • 4.4. Analytics & Virtualization
  • 4.5. Actioning & Business Process Management (BPM)
  • 4.6. Data Governance

Chapter 5: Big Data in Telco Analytics

  • 5.1. How Big is the Market for Telco Analytics?
  • 5.2. Improving Subscriber Experience
    • 5.2.1. Generating a Full Spectrum View of the Subscriber
    • 5.2.2. Creating Customized Experiences and Targeted Promotions
    • 5.2.3. Central 'Big Data' Repository: Key to Customer Satisfaction
    • 5.2.4. Reduce Costs and Increase Market Share
  • 5.3. Building Smarter Networks
    • 5.3.1. Understanding the Usage of the Network
    • 5.3.2. The Magic of Analytics: Improving Network Quality and Coverage
    • 5.3.3. Combining Telco Data with Public Data Sets: Real-Time Event Management
    • 5.3.4. Leveraging M2M for Telco Analytics
    • 5.3.5. M2M, Deep Packet Inspection & Big Data: Identifying & Fixing Network Defects
  • 5.4. Churn/Risk Reduction and New Revenue Streams
    • 5.4.1. Predictive Analytics
    • 5.4.2. Identifying Fraud & Bandwidth Theft
    • 5.4.3. Creating New Revenue Streams
  • 5.5. Telco Analytics Case Studies
    • 5.5.1. T-Mobile USA: Churn Reduction by 50%
    • 5.5.2. Vodafone: Using Telco Analytics to Enable Navigation

Chapter 6: Key Players in the Big Data Market

  • 6.1. Vendor Assessment Matrix
  • 6.2. Apache Software Foundation
  • 6.3. Accenture
  • 6.4. Amazon
  • 6.5. APTEAN (Formerly CDC Software)
  • 6.6. Cisco Systems
  • 6.7. Cloudera
  • 6.8. Dell
  • 6.9. EMC
  • 6.10. Facebook
  • 6.11. GoodData Corporation
  • 6.12. Google
  • 6.13. Guavus
  • 6.14. Hitachi Data Systems
  • 6.15. Hortonworks
  • 6.16. HP
  • 6.17. IBM
  • 6.18. Informatica
  • 6.19. Intel
  • 6.20. Jaspersoft
  • 6.21. Microsoft
  • 6.22. MongoDB (Formerly 10Gen)
  • 6.23. MU Sigma
  • 6.24. Netapp
  • 6.25. Opera Solutions
  • 6.26. Oracle
  • 6.27. Pentaho
  • 6.28. Platfora
  • 6.29. Qliktech
  • 6.30. Quantum
  • 6.31. Rackspace
  • 6.32. Revolution Analytics
  • 6.33. Salesforce
  • 6.34. SAP
  • 6.35. SAS Institute
  • 6.36. Sisense
  • 6.37. Software AG/Terracotta
  • 6.38. Splunk
  • 6.39. Sqrrl
  • 6.40. Supermicro
  • 6.41. Tableau Software
  • 6.42. Teradata
  • 6.43. Think Big Analytics
  • 6.44. Tidemark Systems
  • 6.45. VMware (Part of EMC)

Chapter 7: Market Analysis

  • 7.1. Big Data Revenue: 2014 - 2019
  • 7.2. Big Data Revenue by Functional Area: 2014 - 2019
    • 7.2.1. Supply Chain Management
    • 7.2.2. Business Intelligence
    • 7.2.3. Application Infrastructure & Middleware
    • 7.2.4. Data Integration Tools & Data Quality Tools
    • 7.2.5. Database Management Systems
    • 7.2.6. Big Data Social & Content Analytics
    • 7.2.7. Big Data Storage Management
    • 7.2.8. Big Data Professional Services
  • 7.3. Big Data Revenue by Region 2014 - 2019
    • 7.3.1. Asia Pacific
    • 7.3.2. Eastern Europe
    • 7.3.3. Latin & Central America
    • 7.3.4. Middle East & Africa
    • 7.3.5. North America
    • 7.3.6. Western Europe

Figures

  • Figure 1: The Big Data Value Chain
  • Figure 2: Telco Analytics Investments Driven by Big Data: 2013 - 2019 ($ Million)
  • Figure 3: Big Data Vendor Ranking Matrix 2013
  • Figure 4: Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 5: Big Data Revenue by Functional Area: 2013 - 2019 ($ Million)
  • Figure 6: Big Data Supply Chain Management Revenue: 2013 - 2019 ($ Million)
  • Figure 7: Big Data Supply Business Intelligence Revenue: 2013 - 2019 ($ Million)
  • Figure 8: Big Data Application Infrastructure & Middleware Revenue: 2013 - 2019 ($ Million)
  • Figure 9: Big Data Integration Tools & Data Quality Tools Revenue: 2013 - 2019 ($ Million)
  • Figure 10: Big Data Database Management Systems Revenue: 2013 - 2019 ($ Million)
  • Figure 11: Big Data Social & Content Analytics Revenue: 2013 - 2019 ($ Million)
  • Figure 12: Big Data Storage Management Revenue: 2013 - 2019 ($ Million)
  • Figure 13: Big Data Professional Services Revenue: 2013 - 2019 ($ Million)
  • Figure 14: Big Data Revenue by Region: 2013 - 2019 ($ Million)
  • Figure 15: Asia Pacific Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 16: Eastern Europe Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 17: Latin & Central America Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 18: Middle East & Africa Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 19: North America Big Data Revenue: 2013 - 2019 ($ Million)
  • Figure 20: Western Europe Big Data Revenue: 2013 - 2019 ($ Million)
Back to Top