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市場調查報告書

通訊電信業者的B2B資料收益:巨量資料·分析功能·DaaS(Data-As-A-Service資料服務)市場未來展望

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

出版商 Mind Commerce 商品編碼 323204
出版日期 內容資訊 英文 502 Pages
商品交期: 最快1-2個工作天內
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通訊電信業者的B2B資料收益:巨量資料·分析功能·DaaS(Data-As-A-Service資料服務)市場未來展望 Carrier B2B Data Revenue: Big Data, Analytics, Telecom APIs, and Data as a Service (DaaS) 2015 - 2020
出版日期: 2015年07月17日 內容資訊: 英文 502 Pages
簡介

通訊服務供應商擁有龐大的組織化/未組織化之巨量資訊。藉由活用巨量資料及分析功能,便可產生新的收益可能性。針對各產業用以DaaS(Data-As-A-Service資料服務)提供自家公司數據,組合各產業、企業擁有資料,便可實現各種服務、分析(雲端型設備/服務、企業資料一體化、各種消費者用服務等)透過通訊服務供應商的主導,巨量資料分析市場預測在2014年到2019年年平均成長率(CAGR)將達到將近50%。

本報告提供DaaS(Data as a Service)供應商之通訊電信業者今後的B2B(企業間)通訊收益額預測之質性·量性分析,提供您巨量資料概要和活用案例,通訊服務供應商的現在·未來應用領域,今後的市場收益額預測,主要企業簡介等調查評估。

第1章 簡介

第2章 巨量資料技術和商務案例

  • 巨量資料定義
  • 巨量資料的主要特徵
  • 巨量資料技術
    • Hadoop
    • NoSQL
    • MPP Databases
    • 其他新技術
  • 推動市場要素
    • 資料的規模和多樣性
    • 各種企業·通訊業巨量資料利用的增加
    • 巨量資料用軟體的成熟化
    • Web大企業對巨量資料的持續性投資
  • 市場阻礙因素
    • 隱私和安全性:「大」障礙
    • 勞工的再教育與組織的阻力
    • 缺乏明確的巨量資料策略
    • 技術課題:擴充性(可擴展性)與維持

第3章 巨量資料投資的主要部門

  • 產業用網際網路·M2M(機器間通訊)
    • M2M的巨量資料
    • 各產業的市場機會
  • 零售業·旅館產業
    • 預測準確度的提高與庫存管理
    • 購買模式的決策
    • 飯店產業的使用案例
  • 媒體
    • 社群媒體
    • 社群gaming分析功能
    • 其他產業上社群gaming分析功能的使用
  • 公共事業
    • 運用資料的分析
    • 今後的活用領域
  • 金融服務
    • 詐騙的分析與風險的分析
    • 卡片加盟店的識別計劃
    • 客戶的分類
    • 保險企業
  • 醫療·醫藥品
    • 藥物開發
    • 醫療資料分析
    • 案例研究:心律模式的特定化
  • 電訊企業
    • 電訊企業的分析功能:客戶/利用頻率的分析與服務最佳化
    • 語音通話分析
    • 其他的利用案例
  • 政府·國防安全保障
    • 公共機關的新的使用法的開發
    • 罪犯的追蹤
    • 資訊收集
    • 詐騙探測·產生收入
  • 其他的部門
    • 航空管制
    • 運輸·物流:車隊利用的最佳化
    • 運動:統計資料的實時處理

第4章 巨量資料的價值鏈

  • 巨量資料的價值鏈被細分化到何種程度?
  • 資料收集·提供
  • 資料倉儲和BI(商業智慧)
  • 分析功能和虛擬化
  • Actioning & Business Process Management (BPM)
  • 資料管理

第5章 通訊企業用分析功能的巨量資料

  • 通訊企業用分析功能的市場大小
  • 改善用戶的經驗(體驗)
    • 創造針對所有客戶的服務
    • 客制化體驗的創造與專用宣傳
    • 巨量資料的保管功能核心:顧客滿意度的關鍵
    • 降低成本與市場佔有率擴大
  • 建立更智慧的網路
    • 理解網路的使用形態
    • 分析功能的魔術:提高網絡品質和覆蓋範圍
    • 通訊資料和公共資料集的結合:即時·活動管理
    • 通訊市場上的M2M的活用
    • 與M2M·Deep Packet Inspection(DPI)·巨量資料:確定和固定網絡的缺陷
  • 減輕客戶流失/風險的新收益來源
    • 預見的分析
    • 鎖定詐騙·頻帶盜用
    • 創造新的收益來源
  • 通訊企業用分析功能:案例研究
    • T-Mobile USA:削減50%的客戶流失
    • Vodafone:活用通訊分析功能的實行導航功能

第6章 巨量資料市場主要企業

  • Vendor Assessment Matrix
  • Apache Software Foundation
  • Accenture
  • Amazon
  • APTEAN(舊CDC Software)
  • Cisco Systems
  • Cloudera
  • Dell
  • EMC
  • Facebook
  • GoodData Corporation
  • Google
  • Guavus
  • Hitachi Data Systems
  • Hortonworks
  • HP
  • IBM
  • Informatica
  • Intel
  • Jaspersoft
  • Microsoft
  • MongoDB(舊10Gen)
  • MU Sigma
  • Netapp
  • Opera Solutions
  • Oracle
  • Pentaho
  • Platfora
  • Qliktech
  • Quantum
  • Rackspace
  • Revolution Analytics
  • Salesforce
  • SAP
  • SAS Institute
  • Sisense
  • Software AG/Terracotta
  • Splunk
  • Sqrrl
  • Supermicro
  • Tableau Software
  • Teradata
  • Think Big Analytics
  • Tidemark Systems
  • VMware(EMC的一部分)

第7章 市場分析

  • 巨量資料的市場收益額預測(今後6年份)
  • 收益額預測:各功能領域(今後6年份)
    • 供應鏈管理
    • 商業智慧(BI)
    • 應用基礎設施&中介軟體
    • 資料整合工具·資料品質工具
    • 資料庫管理系統
    • 巨量資料的社群&內容分析
    • 巨量資料的儲存管理
    • 巨量資料的專門服務
  • 收益額預測:各地區(今後6年份)
    • 亞太地區
    • 東歐
    • 南美·中美
    • 中東·非洲
    • 北美
    • 西歐

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目錄

Overview:

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.

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

Table of Contents

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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