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Carrier B2B Data Revenue: Big Data, Analytics, Telecom APIs, and Data as a Service (DaaS) 2015 – 2020

Carrier B2B Data Revenue: Big Data, Analytics, Telecom APIs, and Data as a Service (DaaS) 2015 – 2020

This product is a collection of several individual publications.

Telecommunications service providers acquire and maintain substantial structured and unstructured (Big) data. Leading carriers have centralized Subscriber Data Management (SDM) systems, which consolidate and organize data from various sources such as HLR, HSS, and other data repositories. In addition, carriers have access to a plethora of data from various "Big Data" sources such as OSS/BSS, system monitoring and performance management systems including Self Organizing Networks (SON).

Big Data and related Analytics solutions opens a vast array of applications and opportunities for telecom carriers to offer services in multiple industry verticals. Network operators may sell data in a "Data as a Service" (DaaS) model to various market sectors including retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical. DaaS is defined as any service offered wherein users can access vendor provided databases or host their own databases on vendor managed systems.

Carriers have an excellent opportunity to offer Business-to-Business (B2B) services on a DaaS basis, representing a fast growing secondary and revenue stream. The Big Data driven telecom analytics market is expected to grow at a CAGR of nearly 49% between 2015 and 2020, accounting for $7.6 Billion in annual revenue by 2020. The Telecom APIs market is expected to account for $ 167.5 Billion in global revenues worldwide by 2020, growing at a CAGR of 26 % between 2015 and 2020. The overall DaaS market will reach $271.9B globally by 2020.

This research evaluates telecom data, analytics, APIs, and provides a quantitative and qualitative and assessment of carrier prospects for B2B revenue as a DaaS provider including forecast data and key insights respectively. 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:

DaaS service providers
Telecom service providers
Wireless device manufacturers
Big Data and Analytics companies
Wireless infrastructure companies
Telecom managed service companies
Cloud infrastructure and XaaS providers
Intermediaries and mediation companies

Key Findings:

The Big Data driven telecom analytics market is expected to grow at a CAGR of nearly 49% between 2015 and 2020, accounting for $7.6 Billion in annual revenue by 2020
The Telecom APIs market is expected to account for $ 167.5 Billion in global revenues worldwide by 2020, growing at a CAGR of 26 % between 2015 and 2020
The overall DaaS market will reach $271.9B globally by 2020

Report Benefits:

Forecast data for Big Data, Analytics, Telecom APIs, and DaaS to 2020
Understand DaaS infrastructure challenges for service provider operations
Recognize the role and importance of DaaS as a carrier B2B service offering
Understand the importance of managed systems and best practices for DaaS
Identify carrier Big Data, Analytics, and Telecom API enabled service offerings
Understand Big Data and Analytics vendor landscape, value chain analysis, case studies

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.


Telecom Structured Data, Big Data, and Analytics: Business Case, Analysis and Forecasts 2015 – 2020
1 Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Key Findings
1.4 Target Audience
1.5 Companies Mentioned
2 Big Data Technology and Business Case
2.1 Structured vs. Unstructured Data
2.1.1 Structured Database Services in Telecom
2.1.2 Unstructured Data from Apps and Databases in Telecom
2.1.3 Emerging Hybrid (Structured/Unstructured) Database Services
2.2 Defining Big Data
2.3 Key Characteristics of Big Data
2.3.1 Volume
2.3.2 Variety
2.3.3 Velocity
2.3.4 Variability
2.3.5 Complexity
2.4 Capturing Data through Detection and Social Systems
2.4.1 Data in Social Systems
2.4.2 Detection and Sensors
2.4.3 Sensors in the Consumer Sector
2.4.4 Sensors in Industry
2.5 Big Data Technology
2.5.1 Hadoop
2.5.1.1 MapReduce
2.5.1.2 HDFS
2.5.1.3 Other Apache Projects
2.5.2 NoSQL
2.5.2.1 Hbase
2.5.2.2 Cassandra
2.5.2.3 Mongo DB
2.5.2.4 Riak
2.5.2.5 CouchDB
2.5.3 MPP Databases
2.5.4 Others and Emerging Technologies
2.5.4.1 Storm
2.5.4.2 Drill
2.5.4.3 Dremel
2.5.4.4 SAP HANA
2.5.4.5 Gremlin & Giraph
2.6 Business Drivers for Telecom Big Data and Analytics
2.6.1 Continued Growth of Mobile Broadband
2.6.2 Competition from New Types of Service Providers
2.6.3 New Technology Investment
2.6.4 Need for New KPIs
2.6.5 Artificial Intelligence and Machine Learning
2.7 Market Barriers
2.7.1 Privacy and Security: The ‘Big’ Barrier
2.7.2 Workforce Re-skilling and Organizational Resistance
2.7.3 Lack of Clear Big Data Strategies
2.7.4 Technical Challenges: Scalability and Maintenance
3 Key Big Data Investment Sectors
3.1 Industrial Internet and M2M
3.1.1 Big Data in M2M
3.1.2 Vertical Opportunities
3.2 Retail and Hospitality
3.2.1 Improving Accuracy of Forecasts and 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 and Pharmaceutical
3.6.1 Drug Development
3.6.2 Medical Data Analytics
3.6.3 Case Study: Identifying Heartbeat Patterns
3.7 Telecom Companies
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 and 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 and Revenue Generation
3.9 Other Sectors
3.9.1 Aviation: Air Traffic Control
3.9.2 Transportation and Logistics: Optimizing Fleet Usage
3.9.3 Sports: Real-Time Processing of Statistics
4 The Big Data Value Chain
4.1 Fragmentation in the Big Data Value Chain
4.2 Data Acquisitioning and Provisioning
4.3 Data Warehousing and Business Intelligence
4.4 Analytics and Virtualization
4.5 Actioning and Business Process Management (BPM)
4.6 Data Governance
5 Big Data in Telecom Analytics
5.1 Telecom Analytics Market 2015 - 2020
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 Network Utilization
5.3.2 Improving Network Quality and Coverage
5.3.3 Combining Telecom Data with Public Data Sets: Real-Time Event Management
5.3.4 Leveraging M2M for Telecom Analytics
5.3.5 M2M, Deep Packet Inspection and 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 and Bandwidth Theft
5.4.3 Creating New Revenue Streams
5.5 Telecom Analytics Case Studies
5.5.1 T-Mobile USA: Churn Reduction by 50%
5.5.2 Vodafone: Using Telco Analytics to Enable Navigation
5.6 Carriers, Analytics, and Data as a Service (DaaS)
5.6.1 Carrier Data Management Operational Strategies
5.6.2 Network vs. Subscriber Analytics
5.6.3 Data and Analytics Opportunities to Third Parties
5.6.4 Carriers to offer Data as s Service (DaaS) on B2B Basis
5.6.5 DaaS Planning and Strategies
5.6.6 Carrier Monetization of Data with DaaS
5.7 Opportunities for Carriers in Cloud Analytics
5.7.1 Carrier NFV and Cloud Analytics
5.7.2 Carrier Cloud OSS/BSS Analytics
5.7.3 Carrier Cloud Services, Data, and Analytics
5.7.4 Carrier Performance Management and the Cloud Analytics
6 Structured Data in Telecom Analytics
6.1 Telecom Data Sources and Repositories
6.1.1 Subscriber Data
6.1.2 Subscriber Presence and Location Data
6.1.3 Business Data: Toll-free and other Directory Services
6.1.4 Network Data: Deriving Data from Network Operations
6.2 Telecom Data Mining
6.2.1 Data Sources: Rating, Charging, and Billing Examples
6.2.2 Privacy Issues
6.3 Telecom Database Services
6.3.1 Calling Name Identity
6.3.2 Subscriber Data Management (SDM) Services
6.3.3 Other Data-intensive Service Areas
6.3.4 Emerging Service Area: Identity Verification
6.4 Structured Telecom Data Analytics
6.4.1 Dealing with Telecom Data Fragmentation
6.4.2 Deep Packet Inspection
7 Key Players in the Big Data Market
7.1 Vendor Assessment Matrix
7.2 Apache Software Foundation
7.3 Accenture
7.4 Amazon
7.5 APTEAN (Formerly CDC Software)
7.6 Cisco Systems
7.7 Cloudera
7.8 Dell
7.9 EMC
7.10 Facebook
7.11 GoodData Corporation
7.12 Google
7.13 Guavus
7.14 Hitachi Data Systems
7.15 Hortonworks
7.16 HP
7.17 IBM
7.18 Informatica
7.19 Intel
7.20 Jaspersoft
7.21 Microsoft
7.22 MongoDB (Formerly 10Gen)
7.23 MU Sigma
7.24 Netapp
7.25 Opera Solutions
7.26 Oracle
7.27 ParStream
7.28 Pentaho
7.29 Platfora
7.30 Qliktech
7.31 Quantum
7.32 Rackspace
7.33 Revolution Analytics
7.34 Salesforce
7.35 SAP
7.36 SAS Institute
7.37 Sisense
7.38 Software AG/Terracotta
7.39 Splunk
7.40 Sqrrl
7.41 Supermicro
7.42 Tableau Software
7.43 Teradata
7.44 Think Big Analytics
7.45 Tidemark Systems
7.46 VMware (Part of EMC)
8 Market Analysis
8.1 Market for Structured Telecom Data Services
8.2 Market for Unstructured (Big) Data Services
8.2.1 Big Data Revenue 2015 - 2020
8.2.2 Big Data Revenue by Functional Area 2015 - 2020
8.2.3 Big Data Revenue by Region 2015 - 2020
9 Summary and Recommendations
9.1 Key Success Factors for Carriers
9.1.1 Leverage Real-time Data
9.1.2 Recognize that Analytics is Not Business Intelligence
9.1.3 Provide Data Discovery Services
9.1.4 Provide Big Data and Analytics to Enterprise Customers
9.2 The Role of Intermediaries in the Ecosystem
9.2.1 Cloud and Big Data Intermediation
9.2.2 Security, Communications, Billing, and Settlement
9.2.3 The Case for Data as a Service (DaaS)
10 Appendix: Understanding Big Data Analytics
10.1 What is Big Data Analytics?
10.2 The Importance of Big Data Analytics
10.3 Reactive vs. Proactive Analytics
10.4 Technology and Implementation Approaches
10.4.1 Grid Computing
10.4.2 In-Database processing
10.4.3 In-Memory Analytics
10.4.4 Data Mining
10.4.5 Predictive Analytics
10.4.6 Natural Language Processing
10.4.7 Text Analytics
10.4.8 Visual Analytics
10.4.9 Association Rule Learning
10.4.10 Classification Tree Analysis
10.4.11 Machine Learning
10.4.11.1 Neural Networks
10.4.11.2 Multilayer Perceptron (MLP)
10.4.11.3 Radial Basis Functions
10.4.11.4 Support Vector Machines
10.4.11.5 Naïve Bayes
10.4.11.6 k-nearest Neighbours
10.4.11.7 Geospatial Predictive Modelling
10.4.12 Regression Analysis
10.4.13 Social Network Analysis
Figures
Figure 1: Hybrid Data in Next Generation Applications
Figure 2: Big Data Components
Figure 3: Big Data Sources
Figure 4: Capturing Data from Detection Systems and Sensors
Figure 5: Capturing Data across Sectors
Figure 6: AI Structure
Figure 7: The Big Data Value Chain
Figure 8: Telco Analytics Investments Driven by Big Data: 2015 – 2020
Figure 9: Different Data Types within Telco Environment
Figure 10: Presence-enabled Application
Figure 11: Calling Name (CNAM) Service Operation
Figure 12: Subscriber Data Management (SDM) Ecosystem
Figure 13: Data Fragmented across Telecom Databases
Figure 14: Telecom Deep Packet Inspection Revenue 2015 - 2020
Figure 15: Big Data Vendor Ranking Matrix
Figure 16: Unified Communications Incoming Call Routing
Figure 17: Network Level Outbound Call Management
Figure 18: Big Data Revenue: 2015 – 2020
Figure 19: Big Data Revenue by Functional Area: 2015 – 2020
Figure 20: Big Data Revenue by Region: 2015 – 2020
Figure 21: Data Mediation for Structured and Unstructured Data
Figure 21: Cloud and Big Data Intermediation
Figure 22: Data Security, Billing and Settlement
Figure 24: Big Data as a Service (BDaaS)
Data as a Service (DaaS) Market and Forecasts 2015 – 2020
1 Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Key Findings
1.4 Target Audience
2 DaaS Technologies
2.1 Cloud
2.2 Database Approaches and Solutions
2.2.1 Relational Database Management System (RDBS)
2.2.2 NoSQL
2.2.3 Hadoop
2.2.4 High Performance Computing Cluster (HPCC)
2.2.5 OpenStack
2.3 DaaS and the XaaS Ecosystem
2.4 Open Data Center Alliance
2.5 Market Sizing by Horizontal
3 DaaS Market
3.1 Market Overview
3.1.1 Data-as-a-Service: A movement
3.1.2 Data Structure
3.1.3 Specialization
3.1.4 Vendors
3.2 Vendor Analysis and Prospects
3.2.1 Large Vendors: BDaaS
3.2.2 Mid-sized Vendors
3.2.3 Small Vendors: DaaS and SaaS
3.2.4 Market Size: BDaaS vs. RDBMS
3.3 Market Drivers and Constraints
3.3.1 Drivers
3.3.1.1 Business Intelligence and DaaS Integration
3.3.1.2 The Cloud Enabler DaaS
3.3.1.3 XaaS Drives DaaS
3.3.2 Constraints
3.3.2.1 Issues Relating to Data-as-a-Service Integration
3.4 Barriers and Challenges to DaaS Adoption
3.4.1 Enterprises Reluctance to Change
3.4.2 Responsibility of Data Security Externalized
3.4.3 Security Concerns are Real
3.4.4 Cyber Attacks
3.4.5 Unclear Agreements
3.4.6 Complexity is a Deterrent
3.4.7 Lack of Cloud Interoperability
3.4.8 Service Provider Resistance to Audits
3.4.9 Viability of Third-party Providers
3.4.10 No Move of Systems and Data is without Cost
3.4.11 Lack of Integration Features in the Public Cloud results in Reduced Functionality
3.5 Market Share and Geographic Influence
3.6 Vendors
3.6.1 1010data
3.6.2 Amazon
3.6.3 Clickfox
3.6.4 Datameer
3.6.5 Google
3.6.6 Hewlett-Packard
3.6.7 IBM
3.6.8 Infosys
3.6.9 Microsoft
3.6.10 Oracle
3.6.11 Rackspace
3.6.12 Salesforce
3.6.13 Splunk
3.6.14 Teradata
3.6.15 Tresata
4 DaaS Strategies
4.1 General Strategies
4.1.1 Tiered Data Focus
4.1.2 Value-based Pricing
4.1.3 Open Development Environment
4.2 Specific Strategies
4.2.1 Service Ecosystem and Platforms
4.2.2 Bringing to Together Multiple Sources for Mash-ups
4.2.3 Developing Value-added Services (VAS) as Proof Points
4.2.4 Open Access to all Entities including Competitors
4.2.5 Prepare for Big Opportunities with the Internet of Things (IoT)
4.3 Service Provider Strategies
4.3.1 Telecom Network Operators
4.3.2 Data Center Providers
4.3.3 Managed Service Providers
4.4 Infrastructure Provider Strategies
4.4.1 Enable New Business Models
4.5 Application Developer Strategies
5 DaaS based Applications
5.1 Business Intelligence
5.2 Development Environments
5.3 Verification and Authorization
5.4 Reporting and Analytics
5.5 DaaS in Healthcare
5.6 DaaS and Wearable technology
5.7 DaaS in the Government Sector
5.8 DaaS for Media and Entertainment
5.9 DaaS for Telecoms
5.10 DaaS for Insurance
5.11 DaaS for Utilities and Energy Sector
5.12 DaaS for Pharmaceuticals
5.13 DaaS for Financial Services
6 Market Outlook and Future of DaaS
6.1 Recent Security Concerns
6.2 Cloud Trends
6.2.1 Hybrid Computing
6.2.2 Multi-Cloud
6.2.3 Cloud Bursting
6.3 General Data Trends
6.4 Enterprise Leverages own Data and Telecom
6.4.1 Web APIs
6.4.2 SOA and Enterprise APIs
6.4.3 Cloud APIs
6.4.4 Telecom APIs
6.5 Data Federation Emerges for DaaS
7 Conclusions
8 Appendix
8.1 Structured vs. Unstructured Data
8.1.1 Structured Database Services in Telecom
8.1.2 Unstructured Database Services in Telecom and Enterprise
8.1.3 Emerging Hybrid (Structured/Unstructured) Database Services
8.2 Data Architecture and Functionality
8.2.1 Data Architecture
8.2.1.1 Data Models and Modelling
8.2.1.2 DaaS Architecture
8.2.2 Data Mart vs. Data Warehouse
8.2.3 Data Gateway
8.2.4 Data Mediation
8.3 Master Data Management (MDM)
8.3.1 Understanding MDM
8.3.1.1 Transactional vs. Non-transactional Data
8.3.1.2 Reference vs. Analytics Data
8.3.2 MDM and DaaS
8.3.2.1 Data Acquisition and Provisioning
8.3.2.2 Data Warehousing and Business Intelligence
8.3.2.3 Analytics and Virtualization
8.3.2.4 Data Governance
8.4 Data Mining
8.4.1 Data Capture
8.4.1.1 Event Detection
8.4.1.2 Capture Methods
8.4.2 Data Mining Tools
Figures
Figure 2: Cloud Computing Service Model Stack and Principle Consumers
Figure 3: DaaS across Horizontal and Vertical Segments
Figure 8: Different Data Types and Functions in DaaS
Figure 9: Ecosystem and Platform Model
Figure 10: Ecosystem and Platform Model
Figure 11: DaaS and IoT Mediation for Smartgrid
Figure 12: Internet of Things (IoT) and DaaS
Figure 13: Telecom API Value Chain for DaaS
Figure 14: DaaS, Verification and Authorization
Figure 15: Web APIs
Figure 16: Services Oriented Architecture
Figure 17: Cloud Services, DaaS, and APIs
Figure 18: Telecom APIs
Figure 19: Federated Data vs. Non-Federated Models
Figure 20: Federated Data at Functional Level
Figure 21: Federated Data at City Level
Figure 22: Federated Data at Global Level
Figure 23: Federation Requires Mediation Data
Figure 24: Mediation Data Synchronization
Figure 25: Hybrid Data in Next Generation Applications
Figure 26: Traditional Data Architecture
Figure 27: Data Architecture Modeling
Figure 28: DaaS Data Architecture
Figure 29: Location Data Mediation
Figure 30: Data Mediation in IoT
Figure 31: Data Mediation for Smartgrids
Figure 32: Enterprise Data Types
Figure 33: Data Governance
Figure 34: Data Flow
Figure 35: Processing Streaming Data
Telecom Network API Marketplace: Strategy, Ecosystem, Players and Forecasts 2015 – 2020
1 Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Key Findings
1.4 Target Audience
1.5 Companies Mentioned
2 Telecom Network API Overview
2.1 Defining Network APIs
2.2 Why Carriers are Adopting Telecom Network APIs
2.2.1 Need for New Revenue Sources
2.2.2 B2B Services and Asymmetric Business Models
2.3 Telecom Network API Categories
2.3.1 Web Real-time Communications (WebRTC)
2.3.2 SMS and RCS-E
2.3.3 Presence
2.3.4 MMS
2.3.5 Location
2.3.6 Payments
2.3.7 Voice/Speech
2.3.8 Voice Control
2.3.9 Multimedia Voice Control
2.3.10 M2M
2.3.11 SDM/Identity Management
2.3.12 Subscriber Profile
2.3.13 QoS
2.3.14 ID/SSO
2.3.15 Content Delivery
2.3.16 Hosted UC
2.3.17 Directory
2.3.18 Number Provisioning
2.3.19 USSD
2.3.20 Billing of Non-Digital Goods
2.3.21 Advertising
2.3.22 Collaboration
2.3.23 IVR/Voice Store
2.4 Telecom Network API Business Models
2.4.1 Two-Sided Business Model
2.4.2 Exposing APIs to Developers
2.4.3 Web Mash-ups
2.5 Segmentation
2.5.1 Users by Segment
2.5.2 Workforce Management
2.6 Competitive Issues
2.6.1 Reduced TCO
2.6.2 Open APIs
2.6.3 Configurability
2.7 Percentage of Applications that use APIs
2.8 Telecom API Revenue Potential
2.8.1 Standalone API Revenue vs. Finished Goods Revenue
2.8.2 Telecom API-enabled Mobile VAS Applications
2.8.3 Carrier Focus on Telecom API’s for the Enterprise
2.9 Telecom Network API Usage by Industry Segment
2.10 Telecom Network API Value Chain
2.10.1 Telecom API Value Chain
2.10.2 How the Value Chain Evolve
2.10.3 API Transaction Value Split among Players
2.11 Cost for Different API Transactions
2.12 Volume of API Transactions
3 API Aggregation
3.1 The Role of API Aggregators
3.2 Total Cost Usage for APIs with Aggregators
3.2.1 Start-up Costs
3.2.2 Transaction Costs
3.2.3 Ongoing Maintenance/Support
3.2.4 Professional Services by Intermediaries
3.3 Aggregator API Usage by Category
3.3.1 An LBS Case Study: LOC-AID
3.3.2 Aggregation: Intersection of Two Big Needs
3.3.3 The Case for Other API Categories
3.3.4 Moving Towards New Business Models
4 Enterprise and Telecom API Marketplace
4.1 Data as a Service (DaaS)
4.1.1 Carrier Structured and Unstructured Data
4.1.2 Carrier Data Management in DaaS
4.1.3 Data Federation in the DaaS Ecosystem
4.2 API Market Makers
4.2.1 mashape
4.2.2 Mulesoft
4.3 Need for a New Type of Application Marketplace: CAM
4.3.1 Communications-enabled App Marketplace (CAM)
4.3.2 CAM Market Opportunities and Challenges
5 Telecom API Enabled App Use Cases
5.1 Monetization of Communications-enabled Apps
5.1.1 Direct API Revenue
5.1.2 Data Monetization
5.1.3 Cost Savings
5.1.4 Higher Usage
5.1.5 Churn Reduction
5.2 Use Case Issues
5.2.1 Security
5.2.2 Interoperability
6 Non-Telecom Network APIs and Mash-ups
6.1 Non-Telecom Network APIs
6.1.1 Twitter
6.1.2 Netflix API
6.1.3 Google Maps
6.1.4 Facebook
6.1.5 YouTube
6.1.6 Flickr
6.1.7 eBay
6.1.8 Last.fm
6.1.9 Amazon Web Services
6.1.10 Bing Maps
6.1.11 Yahoo Web Search API
6.1.12 Shopping.com
6.1.13 Salesforce.com
6.2 Mash-ups
6.2.1 BBC News on Mobile
6.2.2 GenSMS emailSMS
6.2.3 Foursquare
6.2.4 Amazon SNS and Nexmo
6.2.5 Triage.me
6.2.6 MappyHealth
6.2.7 Lunchflock
6.2.8 Mobile Time Tracking
6.2.9 Fitsquare
6.2.10 GeoSMS
6.2.11 FONFinder
6.2.12 Pound Docs
6.2.13 140Call
6.2.14 Salesforce SMS
7 Carrier Strategies
7.1 Carrier Market Strategy and Positioning
7.1.1 Increasing API Investments
7.1.2 The Rise of SDM
7.1.3 Telecom API Standardization
7.1.4 Carrier Attitudes towards APIs: U.S vs. Asia Pacific and Western Europe
7.2 Carrier API Programs Worldwide
7.2.1 AT&T Mobility
7.2.2 Verizon Wireless
7.2.3 Vodafone
7.2.4 France Telecom
7.2.5 Telefonica
7.3 Carriers and Internal Telecom API Usage
7.3.1 The Case for Internal Usage
7.3.2 Internal Telecom API Use Cases
7.4 Carriers and OTT Service Providers
7.4.1 Allowing OTT Providers to Manage Applications
7.4.2 Carriers Lack the Innovative Skills to Capitalize on APIs Alone
7.5 Carriers and Value-added Services (VAS)
7.5.1 The Role and Importance of VAS
7.5.2 The Case for Carrier Communication-enabled VAS
7.5.3 Challenges and Opportunities for Carriers in VAS
8 API enabled App Developer Strategies
8.1 A Critical Asset to Developers
8.2 Stimulating the Growth of API Releases
8.3 Working alongside Carrier Programs
8.4 Developer Preferences: Google vs Carriers
9 Telecom API Vendor Strategies
9.1 Positioning as Enablers in the Value Chain
9.2 Moving Away from a Box/Product Supplier Strategy
9.3 Telecom API Companies and Solutions
9.3.1 Alcatel Lucent
9.3.2 UnboundID
9.3.3 Twilio
9.3.4 LOC-AID
9.3.5 Placecast
9.3.6 Samsung
9.3.7 AT&T Mobility
9.3.8 Apigee
9.3.9 2600 Hz
9.3.10 Callfire
9.3.11 Plivo
9.3.12 Tropo (now part of Cisco)
9.3.13 Urban Airship
9.3.14 Voxeo (now Aspect Software)
9.3.15 TeleStax
9.3.16 Intel
9.3.17 Competitive Differentiation
10 Market Analysis and Forecasts
10.1 Telecom Network API Revenue 2015 - 2020
10.2 Telecom Network APIs Revenue by API Category 2015 – 2020
10.2.1 Messaging API Revenues
10.2.2 LBS API Revenues
10.2.3 SDM API Revenues
10.2.4 Payment API Revenues
10.2.5 Internet of Things (IoT) API Revenues
10.2.6 Other API Revenues
10.3 Telecom API Revenue by Region 2015 - 2020
10.3.1 Asia Pacific
10.3.2 Eastern Europe
10.3.3 Latin & Central America
10.3.4 Middle East & Africa
10.3.5 North America
10.3.6 Western Europe
11 Technology and Market Drivers for Future API Market Growth
11.1 Service Oriented Architecture (SOA)
11.2 Software Defined Networks (SDN)
11.3 Virtualization
11.3.1 Network Function Virtualization (NFV)
11.3.2 Virtualization beyond Network Functions
11.4 The Internet of Things (IoT)
11.4.1 IoT Definition
11.4.2 IoT Technologies
11.4.3 IoT Applications
11.4.4 IoT Solutions
11.4.5 IoT, DaaS, and APIs (Telecom and Enterprise)
12 Expert Opinion: TeleStax
13 Expert Opinion: Twilio
14 Expert Opinion: Point.io
15 Expert Opinion: Nexmo
16 Appendix
16.1 Research Methodology
16.2 Telecom API Definitions
16.3 More on Telecom APIs and DaaS
16.3.1 Tiered Data Focus
16.3.2 Value-based Pricing
16.3.3 Open Development Environment
16.3.4 Specific Strategies
16.3.4.1 Service Ecosystem and Platforms
16.3.4.2 Bringing to Together Multiple Sources for Mash-ups
16.3.4.3 Developing Value-added Services (VAS) as Proof Points
16.3.4.4 Open Access to all Entities including Competitors
16.3.4.5 Prepare for Big Opportunities with the Internet of Things (IoT)
Figures
Figure 1: Wireless Carrier Assets
Figure 2: Telecom API: Standalone vs. Finished Services
Figure 3: RCS and Telecom API Integration
Figure 4: RCS Revenue Forecast
Figure 5: Business vs. Consumer Telecom API Focus
Figure 6: Enterprise Dashboard
Figure 7: Enterprise Dashboard App Example
Figure 8: Telecom Network API Value Chain
Figure 9: Value Split among Aggregators, Carriers and Enterprise for API Transactions: 2012 - 2019
Figure 10: API Transaction Costs (US Cents) 2012 - 2019
Figure 11: Volume of API Transactions for a Tier 1 Carrier (Billions per Month): 2015 - 2020
Figure 12: Cloud Services and APIs
Figure 13: GSMA OneAPI: Benefits to Stakeholders
Figure 14: AT&T Wireless API Catalog
Figure 15: Verizon Wireless API Program
Figure 16: France Telecom (Orange) APIs
Figure 17: Telefonica APIs
Figure 18: Carrier Internal Use of Telecom APIs
Figure 19: UnboundID’s Portfolio of Services
Figure 20: Twilio’s Portfolio of Services
Figure 21: LOC-AID Exchange Server Architecture
Figure 22: Placecast’s ShopAlerts Solution
Figure 23: Apigee Portfolio of Services
Figure 24: Telecom API Revenue (USD Billions) 2015 - 2020
Figure 25: Telecom API Revenue (USD Billions) by API Category 2015 - 2020
Figure 26: Messaging APIs Revenue (USD Billions) 2015 - 2020
Figure 27: LBS APIs Revenue (USD Billions) 2015 - 2020
Figure 28: SDM APIs Revenue (USD Billions) 2015 - 2020
Figure 29: Payment APIs Revenue (USD Billions) 2015 - 2020
Figure 30: IoT API Revenue (USD Billions) 2015 - 2020
Figure 31: APIs Revenue for Other Categories (USD Billions) 2015 - 2020
Figure 32: Telecom API Revenue (USD Billions) by Region 2015 - 2020
Figure 33: Telecom API Revenue (USD Billions) Asia Pacific 2015 - 2020
Figure 34: Telecom API Revenue (USD Billions) Eastern Europe 2015 - 2020
Figure 35: Telecom API Revenue (USD Billions) Latin & Central America 2015 - 2020
Figure 36: Telecom API Revenue (USD Billions) Middle East & Africa 2015 - 2020
Figure 37: Telecom API Revenue (USD Billions) North America 2015 - 2020
Figure 38: Telecom API Revenue (USD Billions) Western Europe 2015 - 2020
Figure 39: Services Oriented Architecture
Figure 40: Growth of Connected Devices
Figure 41: IoT and Telecom API Topology
Figure 42: Telestax App Store Funnel
Figure 43: On-Premise vs. Twilio
Figure 44: Point.io and API Ecosystem
Figure 45: Different Data Types and Functions in DaaS
Figure 46: Ecosystem and Platform Model
Figure 47: Telecom API and Internet of Things Mediation
Figure 48: DaaS and IoT Mediation for Smartgrid

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