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市場調査レポート

通信キャリアのB2Bデータ収益:ビッグデータ・解析機能・DaaS(サービスとしてのデータ)市場の将来展望

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

発行 Mind Commerce Publishing LLC 商品コード 323204
出版日 ページ情報 英文 502 Pages
納期: 即日から翌営業日
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本日の銀行送金レート: 1USD=106.12円で換算しております。
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通信キャリアのB2Bデータ収益:ビッグデータ・解析機能・DaaS(サービスとしてのデータ)市場の将来展望 Carrier B2B Data Revenue: Big Data, Analytics, Telecom APIs, and Data as a Service (DaaS) 2015 - 2020
出版日: 2015年07月17日 ページ情報: 英文 502 Pages
概要

通信サービスプロバイダーは莫大な量の組織化された/されてない(ビッグ)データを保有しています。ビッグデータや解析機能の活用により、新たな収益の可能性が生まれています。各産業向けに自社データをDaaS(サービスとしてのデータ)として提供し、各産業・企業が保有するデータを組み合わせて様々なサービス・分析(クラウド活用型インフラ/サービス、企業データの一体化、各種の消費者向けサービスなど)が可能となります。通信サービスプロバイダー主導によるビッグデータ解析市場は、2014〜2019年に50%近くの年平均成長率(CAGR)に達する見通しです。

当レポートでは、DaaS(Data as a Service)プロバイダーとしての通信キャリアの今後のB2B(企業間)通信収益額の見通しについて質的・量的な分析を行い、ビッグデータの概要や活用事例、通信サービスプロバイダーにおける現在・将来の活用分野、今後の市場収益額の見通し、主要企業のプロファイルなどを調査・推計しております。

第1章 イントロダクション

第2章 ビッグデータ技術とビジネスケース

  • ビッグデータの定義
  • ビッグデータの主な特徴
  • ビッグデータ技術
    • Hadoop
    • NoSQL
    • MPP Databases
    • その他の新規技術
  • 市場促進要因
    • データの規模と多様性
    • 各種企業・通信業界でのビッグデータ利用の増加
    • ビッグデータ用ソフトウェアの成熟化
    • Web大手企業によるビッグデータへの継続的投資
  • 市場阻害要因
    • プライバシーとセキュリティ:「ビッグな」障害
    • 労働者の再教育と組織的抵抗
    • 明確なビッグデータ戦略の欠如
    • 技術的課題:拡張性(スケーラビリティ)と維持

第3章 ビッグデータに投資している主な部門

  • 産業用インターネット・M2M(機械間通信)
    • M2Mにおけるビッグデータ
    • 産業別の市場機会
  • 小売業・ホスピタリティ産業
    • 予測精度の向上と在庫管理
    • 購入パターンの決定
    • ホスピタリティ業界での使用事例
  • メディア
    • ソーシャルメディア
    • ソーシャルゲーミング解析機能
    • 他産業におけるソーシャルゲーミング解析機能の使用
  • ユーティリティ
    • 運用データの解析
    • 今後の活用領域
  • 金融サービス
    • 詐欺の分析とリスクのプロファイリング
    • カード加盟店拠出によるポイントプログラム
    • 顧客の分類
    • 保険企業
  • 医療・医薬品
    • 薬剤開発
    • 医療データ解析
    • ケーススタディ:心拍数パターンの特定化
  • 電気通信企業
    • 電気通信企業の解析機能:顧客/利用頻度のプロファイリングとサービス最適化
    • 音声通話解析
    • 他の利用事例
  • 政府・国土安全保障
    • 公共機関での新たな使用法の開発
    • 犯罪者の追跡
    • 情報収集
    • 詐欺探知・収益創出
  • その他の部門
    • 航空管制
    • 輸送・ロジスティクス:フリート利用の最適化
    • スポーツ:統計データのリアルタイム処理

第4章 ビッグデータのバリューチェーン

  • ビッグデータのバリューチェーンはどこまで細分化されているのか?
  • データ収集・提供
  • データウェアハウスとBI(ビジネスインテリジェンス)
  • 解析機能と仮想化
  • Actioning & Business Process Management (BPM)
  • データ管理

第5章 通信企業向け解析機能におけるビッグデータ

  • 通信企業向け解析機能の市場の大きさ
  • 加入者の経験(エクスペリエンス)の改善
    • あらゆる顧客向けのサービスの創出
    • カスタム化体験の創出と専用プロモーション
    • ビッグデータの保管機能の中核:顧客満足度の鍵
    • コスト削減と市場シェア拡大
  • よりスマートなネットワークの構築
    • ネットワークの使用形態の理解
    • 解析機能のマジック:ネットワークの品質・範囲の改善
    • 通信データと公共データセットの結合:リアルタイム・イベント管理
    • 通信市場でのM2Mの活用
    • M2M・ディープバケットインスペクション(DPI)・ビッグデータ:ネットワークの弱点の特定化と補修
  • チャーン/リスクの軽減と新たな収益源
    • 予見的解析昨日
    • 詐欺・周波数帯盗用の特定化
    • 新たな収益源の創出
  • 通信企業向け解析機能:ケーススタディ
    • T-Mobile USA:チャーンの50%削減
    • Vodafone:通信解析機能を活用したナビゲーション機能の実装

第6章 ビッグデータ市場の主要企業

  • Vendor Assessment Matrix
  • Apache Software Foundation
  • Accenture
  • Amazon
  • APTEAN(旧CDC Software)
  • Cisco Systems
  • Cloudera
  • Dell
  • EMC
  • Facebook
  • GoodData Corporation
  • Google
  • Guavus
  • 日立データシステムズ
  • Hortonworks
  • HP
  • IBM
  • Informatica
  • Intel
  • Jaspersoft
  • Microsoft
  • MongoDB(旧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の一部)

第7章 市場分析

  • ビッグデータの市場収益額見通し(今後6年間分)
  • 収益額見通し:機能分野別(今後6年間分)
    • サプライチェーン管理
    • ビジネスインテリジェンス(BI)
    • アプリケーションインフラ&ミドルウェア
    • データ統合ツール・データ品質ツール
    • データベース管理システム
    • ビッグデータによるソーシャル&コンテンツ解析
    • ビッグデータによるストレージ管理
    • ビッグデータによる専門サービス
  • 収益額見通し:地域別(今後6年間分)
    • アジア太平洋地域
    • 東欧
    • ラテンアメリカ・中米
    • 中東・アフリカ
    • 北米
    • 西欧

図表一覧

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目次

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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