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

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

This product is a collection of several individual publications.

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

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.

Target Audience:

Global wireless carriers
Telecom equipment providers
Global infrastructure suppliers
Communications component providers
Cloud services and datacenter companies
Smartgrid and energy management companies
Sovereign investment funds, hedge funds and private
Big data, analytics, and information processing companies

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

General Methodology

Mind Commerce Publishing's research methodology encompasses input from a wide variety of sources.

We rely heavily upon our Subject Matter Experts (SME) in terms of their market knowledge, unique perspective, and vision. We utilize SME industry contacts as well as previous customers and participants in our market surveys and interactive interviews.

In addition, we rely upon our extensive internal database, which contains modeling, qualitative analysis, and quantitative data. We review secondary sources and compare to our primary sources to update previous findings (for prior version reports) and/or compile baseline information for technology and market modeling.

We share preliminary models with industry contacts (select previous clients, experts, and thought leaders) to verify the veracity of initial modeling. Prior to final report production (analysis, findings, and conclusions), we engage in an internal review with internal SMEs as well as cross-expertise, senior staff members to challenge results.

We believe that forecasts should be prepared as part of an integrated process which involves both quantitative as well as qualitative factors. We follow the following 3-step process for forecasting.

Forecasting Methodology

Step 1 - Forecasts Input: The inputs for the present and historical revenues are derived from industry players. Financial and other quantitative data for individual sub-market categories are derived from original research and tested with interviews with major industry constituents.

Step 2 - Forecasting of Future Years: Mind Commerce extends forecasts based on a variety of factors including demand drivers as well as supply side data. Key success factors and assumptions are considered.

Step 3 - Validation of Data: The final step is to validate projections, which is accomplished in consultation with both internal and external industry experts, including both topic and regional experts. Adjustments are made to the forecasts based on factors identified throughout this process.


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
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
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 ©
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 87 
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 –
1 Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Key Findings
1.4 Target Audience
1.5 Companies Mentioned
2 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
3 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
4 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
5 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
6 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)
7 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)

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