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市場調查報告書

通訊服務供應商的B2B資訊服務:結構化·巨量資料,分析,雲端服務,資料即服務 (DaaS) 及通訊API

Communication Service Provider B2B Data Services: Structured and Big Data, Analytics, Cloud Services, Data as a Service, and Telecom APIs

出版商 Mind Commerce 商品編碼 344804
出版日期 內容資訊 英文 772 Pages
商品交期: 最快1-2個工作天內
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通訊服務供應商的B2B資訊服務:結構化·巨量資料,分析,雲端服務,資料即服務 (DaaS) 及通訊API Communication Service Provider B2B Data Services: Structured and Big Data, Analytics, Cloud Services, Data as a Service, and Telecom APIs
出版日期: 2015年11月09日 內容資訊: 英文 772 Pages
簡介

本報告提供通訊服務供應商 (CSP) 的B2B資訊服務市場相關的總括性調查,提供您CSP所面臨的主要問題及包含巨量資料分析,通訊API,資料即服務 (DaaS) 的提供服務上重要問題分析·預測,加上主要企業簡介,為您概述為以下內容。

通訊結構化資料,巨量資料,及分析:商務案例,分析及預測 2015-2020年

第1章 簡介

第2章 巨量資料技術·商務案例

  • 結構化 vs. 非結構化資料
  • 巨量資料定義
  • 巨量資料的主要特徵
  • 透過檢測·社群系統的獲得資料
  • 巨量資料技術
  • 通訊巨量資料·分析的商務促進要素
  • 市場障礙

第3章 主要巨量資料投資部門

  • 產業用網際網路·M2M
  • 零售·飯店
  • 媒體
  • 公共事業
  • 金融服務
  • 醫療·醫藥品
  • 通訊企業
  • 政府·國防安全保障
  • 其他的部門

第4章 巨量資料的價值鏈

  • 巨量資料的價值鏈細分化
  • 資料獲得·供應
  • 資料存放系統·商業智慧
  • 分析·虛擬化
  • Actioning·商務流程管理 (BPM)
  • 資料管治

第5章 通訊分析的巨量資料

  • 通訊分析市場
  • 用戶體驗的改善
  • 建立更智慧的網路
  • 降低客戶流失/風險及新收益源
  • 通訊分析的案例研究
  • 電信業者,分析及資料即服務 (DaaS)
  • 雲端分析上電信業者的機會

第6章 通訊分析的結構化資料

  • 通訊資料來源·庫
  • 通訊資料探勘
  • 通訊數據庫服務
  • 結構化通訊資料分析

第7章 巨量資料市場上主要企業

第8章 市場分析

  • 結構化通訊資訊服務市場
  • 非結構化 (BIG) 資訊服務市場

第9章 結論·建議

  • 對電信業者而言的主要成功因素
  • 生態系統上中介所扮演的角色

第10章 附錄:巨量資料分析的理解

圖表

通訊API市場:策略,生態系統,企業及預測 2015-2020年

第1章 簡介

第2章 通訊網路API概要

第3章 API聚合

第4章 企業·通訊API市場

第5章 通訊API支援應用的利用案例

第6章 非通訊網路API·混搭

第7章 電信業者策略

第8章 開發支援API應用程序的策略

第9章 通訊API的供應商策略

第10章 市場分析·預測

第11章 未來的API市場促使成長的技術·市場成長的促進要素

第12章 專家的見解:TeleStax

第13章 專家的見解:Twilio

第14章 專家的見解:Point.io

第15章 專家的見解:Nexmo

第16章 附錄

圖表

雲端應用市場 2015-2020年

第1章 摘要整理

第2章 雲端運算概要

第3章 雲端服務分析

第4章 雲端的垂直產業

第5章 雲端應用服務市場預測

第6章 雲端應用服務的供應商分析

第7章 電信業者的雲端機會

第8章 結論·建議

圖表

全球雲端運算:基礎設施,平台,及服務 2015-2020年

第1章 簡介

第2章 概要

第3章 硬體設備·軟體

第4章 未來的雲端運算應用

第5章 企業的建議

第6章 雲端服務供應商的建議

第7章 全球雲端運算市場預測

第8章 雲端引進的障礙·課題

圖表

資料即服務 (DaaS) 市場·預測 2015-2020年

第1章 簡介

第2章 DaaS技術

第3章 DaaS市場

第4章 DaaS策略

第5章 DaaS型應用

第6章 DaaS的市場展望·未來

第7章 結論

第8章 附錄

圖表

目錄

Overview:

Communication Service Providers (CSP) are facing profound changes to their business due to many factors including diminished margins on core services, competition from OTT players, and the need to integrate next generation technologies (such as SDN and NFV) to become more efficient. At the same time, CSPs are faced with additional capital costs due to implementation of key initiatives such as Big Data Analytics and IoT.

Recognizing that the need to generate new high-margin revenue streams, leading CSPs are seeking new revenue models based on leveraging their network and subscriber data assets. Telecom Data as a Service (TDaaS) is one of those new models in which CSPs offer Data as a Service (DaaS) to various third party business on an anonymized basis. For example, Verizon, Sprint, Telefonica and other carriers have partnered with firms including SAP, IBM, HP and AirSage to manage, package and sell various levels of data to marketers and other clients.

This research represents the most comprehensive analysis, insights, and data addressing the CSP B2B Data Services marketplace. It includes evaluation of key issues facing CSPs as well as analysis of all critical areas for service delivery including Big Data Analytics, Telecom APIs, and Data as a Service (DaaS) with forecasts for 2015 - 2020. 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:

  • Cloud services companies
  • Data infrastructure providers
  • Communication service providers
  • Network and application integrators
  • Intermediaries and mediation companies
  • Major enterprise and businesses of all types
  • Investors in the CSP B2B Data Services Ecosystem

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)

Telecom 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

Cloud Application Marketplace 2015-2020

1.0. EXECUTIVE SUMMARY

2.0. OVERVIEW OF CLOUD COMPUTING

  • 2.1. UNDERSTANDING CLOUD COMPUTING
    • 2.1.1. CLOUD COMPUTING SERVICES
  • 2.2. CLOUD FOUNDATIONS
    • 2.2.1. CATEGORIES OF CLOUD COMPUTING DEPLOYMENT MODEL
    • 2.2.2. GRID COMPUTING
    • 2.2.3. GRID COMPUTING MARKET SEGMENTATION
  • 2.3. CLOUD TECHNOLOGIES AND ARCHITECTURE
    • 2.3.1. SOFTWARE DEFINED NETWORKING (SDN)
    • 2.3.2. SDN DEPLOYMENT MODELS
    • 2.3.3. VIRTUALIZATION (SERVER VS. HARDWARE VS. DESKTOP VS. STORAGE)
  • 2.4. CLOUD COMPUTING AND VIRTUALIZATION
  • 2.5. MOVING BEYOND CLOUD COMPUTING
    • 2.5.1. A 'GLOCAL' CLOUD
  • 2.6. RISE OF THE CLOUD-BASED NETWORKED ENTERPRISE
  • 2.7. GENERAL CLOUD SERVICE ENABLERS
    • 2.7.1. WIRELESS BROADBAND CONNECTIVITY
    • 2.7.2. SECURITY SOLUTIONS
    • 2.7.3. PRESENCE AND LOCATION
  • 2.8. PERSONAL CLOUD SERVICE ENABLERS
    • 2.8.1. IDENTITY MANAGEMENT
    • 2.8.2. PREFERENCE MANAGEMENT

3.0. CLOUD SERVICE ANALYSIS

  • 3.1. CLOUD SERVICE SEGMENTATION
    • 3.1.1. BUSINESS TO BUSINESS (B2B)
    • 3.1.2. BUSINESS TO CONSUMER (B2C)
  • 3.2. CORE CLOUD SERVICES
    • 3.2.1. INFRASTRUCTURE AS A SERVICE (IAAS)
    • 3.2.2. PLATFORM AS A SERVICE (PAAS)
    • 3.2.3. SOFTWARE AS A SERVICE (SAAS)
    • 3.2.4. DIFFERENCES BETWEEN IAAS, SAAS, AND PAAS
  • 3.3. EMERGING MODELS: XAAS (EVERYTHING AS A SERVICE)
    • 3.3.1. BUSINESS PROCESS AS A SERVICE (BPAAS)
    • 3.3.2. COMMUNICATION AS A SERVICE (CAAS)
    • 3.3.3. MONITORING AS A SERVICE (MAAS)
    • 3.3.4. NETWORK-AS-A-SERVICE (NAAS)
    • 3.3.5. STORAGE AS A SERVICE (SAAS)
    • 3.3.6. DATA AS A SERVICE (DAAS)
  • 3.4. DATA AS A SERVICE ECOSYSTEM
    • 3.4.1. THE DRIVERS OF DATA-AS-A-SERVICE
    • 3.4.2. BUSINESS INTELLIGENCE AND DAAS INTEGRATION
    • 3.4.3. THE CLOUD ENABLER DAAS
    • 3.4.4. XAAS DRIVES DAAS
    • 3.4.5. THE DAAS ECOSYSTEM
    • 3.4.6. DAAS ELEMENTS
    • 3.4.7. THE ROLE OF DATA MARTS
    • 3.4.8. BEST PRACTICES IN DAAS
    • 3.4.9. BENEFITS OF DAAS
    • 3.4.10. CHALLENGES OF DATA AS A SERVICE
    • 3.4.11. APIS AND DATABASE
    • 3.4.12. THE NEED FOR FEDERATED DATABASE MODEL
  • 3.5. ENTERPRISE RESOURCE PLANNING IN THE CLOUD
  • 3.6. SUPPLY CHAIN MANAGEMENT IN THE CLOUD

4.0. INDUSTRY VERTICALS IN THE CLOUD

  • 4.1. FINANCE AND BANKING IN THE CLOUD
    • 4.1.1. AGILITY, EFFICIENCY, AND SIMPLIFIED DELIVERY
    • 4.1.2. PRIORITIZING THE CLOUD
  • 4.2. RETAIL IN THE CLOUD
  • 4.3. HEALTHCARE IN THE CLOUD
    • 4.3.1. KEY BENEFITS OF CLOUD TECHNOLOGY
  • 4.4. TELECOMMUNICATIONS IN THE CLOUD
    • 4.4.1. OPPORTUNITIES AND CHALLENGES
    • 4.4.2. SOLUTIONS
  • 4.5. GOVERNMENT AND DEFENSE IN THE CLOUD
    • 4.5.1. PROS AND CONS OF THE FEDERAL CLOUD
  • 4.6. WORKFORCE IN THE CLOUD
    • 4.6.1. HUMAN CAPITAL MANAGEMENT IN THE CLOUD
    • 4.6.2. TRAINING AND EDUCATION IN THE CLOUD
    • 4.6.3. COLLABORATION IN THE CLOUD
    • 4.6.4. OFFICE AUTOMATION IN THE CLOUD
  • 4.7. CUSTOMERS IN THE CLOUD
    • 4.7.1. CUSTOMER RELATIONSHIP IN THE CLOUD
    • 4.7.2. COMMERCE AND PAYMENTS IN THE CLOUD
  • 4.8. EMERGING CLOUD BASED APPLICATIONS
    • 4.8.1. B2B APPLICATIONS
    • 4.8.2. BIG DATA AS A SERVICE (BDAAS)
    • 4.8.3. B2C APPLICATIONS
    • 4.8.4. ENTERTAINMENT IN THE CLOUD: TV, VIDEO, GAMING AND MORE
  • 4.9. THE FUTURE OF CLOUD SERVICES
    • 4.9.1. EVERYTHING AS A SERVICE
    • 4.9.2. HOW XAAS DECREASES COSTS AND MAKES EVERYTHING FIT TOGETHER
  • 4.10. DATA CENTER PROVIDERS
  • 4.11. VIRTUALIZATION: ROLE AND IMPACT
    • 4.11.1. TYPES OF VIRTUALIZATION
    • 4.11.2. HOW VIRTUALIZATION AFFECTS COST STRUCTURES

5.0. CLOUD APPLICATION SERVICE MARKET FORECAST

  • 5.1. CLOUD SERVICE MARKET REVENUE FORECAST 2015-2020
  • 5.2. CLOUD SERVICE MARKET REVENUE BY TPES 2015-2020
  • 5.3. CLOUD SERVICE MARKET REVENUE BY CORE SEGMENTS OR MODELS 2015-2020
  • 5.4. CLOUD SAAS MARKET REVENUE BY SEGMENTS 2015-2020
  • 5.5. CLOUD PAAS MARKET REVENUE BY SEGMENTS 2015-2020
    • 5.5.1. CLOUD PAAS MARKET REVENUE BY SUB-SEGMENTS 2015-2020
  • 5.6. CLOUD IAAS MARKET REVENUE BY SEGMENTS 2015-2020
  • 5.7. PUBLIC CLOUD SERVICES MARKET REVENUE BY SEGMENTS 2015-2020
    • 5.7.1. PUBLIC CLOUD MANAGEMENT & SECURITY SERVICES MARKET REVENUE BY SEGMENTS 2015-2020
    • 5.7.2. PUBLIC CLOUD BPAAS SERVICES MARKET REVENUE BY SEGMENTS 2015-2020
  • 5.8. CLOUD SERVICE MARKET REVENUE BY GEOGRAPHIC REGION 2015-2020
  • 5.9. CLOUD APPLICATION SERVICE REVENUE BY INDUSTRY VERTICAL 2015-2020
  • 5.10. CLOUD APPLICATION ADOPTION TREND AMONG PERCENT OF ORGANIZATIONS BY DEPLOYMENT MODELS 2015-2020
  • 5.11. CLOUD APPLICATION ADOPTION TREND AMONG PERCENT OF ORGANIZATIONS BY INDUSTRY VERTICALS 2015-2020
  • 5.12. CLOUD INVESTMENT PERCENT TO INDUSTRY APPLICATIONS 2015
  • 5.13. BENEFITS OF CLOUD APPLICATION SERVICE ADOPTION OVER IN-HOUSE IT SERVICES
  • 5.14. PRIVATE CLOUD STORAGE SUBSCRIPTION FORECAST 2015-2020

6.0. CLOUD APPLICATION SERVICE VENDOR ANALYSIS

  • 6.1. OFFICE AUTOMATION APPLICATION
    • 6.1.1. ZOHO
    • 6.1.2. TECHINLINE
    • 6.1.3. WINDOWS LIVE MESH
    • 6.1.4. DROPBOX
    • 6.1.5. LOGMEIN
    • 6.1.6. MICROSOFT OFFICE 365
    • 6.1.7. NOODLE
  • 6.2. CRM APPLICATIONS
    • 6.2.1. ADDRESSTWO
    • 6.2.2. ALLCLIENTS
    • 6.2.3. MAXIMIZER
    • 6.2.4. SALESCLOUD FROM SALESFORCE
    • 6.2.5. SALESNEXUS
  • 6.3. DATA CENTER APPLICATIONS
    • 6.3.1. GOOGLE
    • 6.3.2. MICROSOFT
    • 6.3.3. SWITCH SUPER NAP
    • 6.3.4. RANGE INTERNATIONAL INFORMATION HUB
  • 6.4. CORE CLOUD SERVICE PROVIDERS
    • 6.4.1. AMAZON
    • 6.4.2. VERIZON
    • 6.4.3. IBM
    • 6.4.4. SALESFORCE.COM
    • 6.4.5. CSC
    • 6.4.6. CENTURYLINK
    • 6.4.7. SAVVIS
    • 6.4.8. JOYENT
    • 6.4.9. MICROSOFT
    • 6.4.10. RACKSPACE
    • 6.4.11. FUJITSU
    • 6.4.12. HP
  • 6.5. CLOUD NETWORK OPERATORS
    • 6.5.1. CHINA MOBILE LIMITED
    • 6.5.2. VODAFONE GROUP
    • 6.5.3. TELENOR GROUP
    • 6.5.4. AMERICA MOVIL
  • 6.6. ENTERPRISE CLOUD APPLICATION
    • 6.6.1. SALESFORCE.COM
    • 6.6.2. BOX
    • 6.6.3. CRASHPLAN
    • 6.6.4. AMAZON WEB SERVICES
    • 6.6.5. EASY VISTA

7.0. CARRIER CLOUD OPPORTUNITY

  • 7.1. CLOUD INFRASTRUCTURE AND SERVICES IN TELECOMMUNICATIONS
    • 7.1.1. CLOUD RAN
  • 7.2. MOBILE CONSUMER CLOUD SERVICES
    • 7.2.1. CONSUMER MOBILITY AND THE CLOUD: STATISTICS AND FORECASTS
  • 7.3. COMMERCIAL CONSIDERATIONS
    • 7.3.1. WHAT CONSUMERS WILL STORE IN AND ACCESS FROM THE CLOUD
    • 7.3.2. WHAT DEVICES CONSUMERS WILL USE TO ACCESS THE CLOUD
    • 7.3.3. WHERE AND HOW CONSUMERS WILL ACCESS THE CLOUD
    • 7.3.4. WHAT COMPANIES DO CONSUMERS IDENTIFY WITH CLOUD SERVICES
    • 7.3.5. CONSUMER WILLINGNESS TO PAY FOR PERSONAL CLOUD SERVICES
  • 7.4. KEY CONCERNS AND SOLUTIONS FOR PERSONAL CLOUD SERVICES
    • 7.4.1. LTE AND ANYWHERE, ANYTIME, ANY DEVICE ACCESS
    • 7.4.2. LTE DRIVES CLOUD GROW ACCELERATION VIA USER GENERATED CONTENT (UGC)
    • 7.4.3. DIGITAL RIGHTS MANAGEMENT (DRM)
    • 7.4.4. NETWORK AND DEVICE OPTIMIZATION
    • 7.4.5. CLOUD DATA SECURITY
    • 7.4.6. IDENTITY MANAGEMENT FOR CLOUD SERVICES
    • 7.4.7. CLOUD SERVICES BROKERING AND CLOUD MEDIATION
  • 7.5. MOBILE NETWORK OPERATOR VAS APPLICATION VS. OTT APPLICATIONS
  • 7.6. TELECOM APIS AND THE CLOUD
    • 7.6.1. ROLE OF API'S IN THE CLOUD
    • 7.6.2. ENTERPRISE API PROVIDERS AND CLOUD SERVICES
    • 7.6.3. TELECOM API'S AND THE CLOUD
  • 7.7. GREATER MOBILE CLOUD COMPUTING
    • 7.7.1. BYOC (BRING YOUR OWN CLOUD) AND INCREASED SECURITY
  • 7.8. CARRIER CLOUD SERVICE STRATEGY
    • 7.8.1. CONSUMER CLOUD SERVICES KEY TO GROWTH IN CARRIER DATA SERVICES
    • 7.8.2. CARRIER CLOUD SERVICES TO DRIVE VALUE-ADDED SERVICES GROWTH
    • 7.8.3. PERSONAL CLOUD SERVICES TO IMPROVE CARRIER TOP LINE REVENUE AND PROFITS
  • 7.9. TELECOM BENEFITS OF OFFERING CLOUD SERVICES
    • 7.9.1. VALUE PROPOSITION FOR TELECOM
    • 7.9.2. WEB-BASED APPLICATIONS PROMOTE IT INDEPENDENCE
    • 7.9.3. CLOUD-BASED MANAGED SERVICES PRODUCES REVENUE
    • 7.9.4. INCREASE DATA CENTER EFFICIENCY AND OPERATIONS
    • 7.9.5. DIFFERENTIATING SERVICE PROVIDERS
  • 7.10. CARRIER ADVANTAGES IN CLOUD ECOSYSTEM
    • 7.10.1. SERVICE-ORIENTATION
    • 7.10.2. PERFORMANCE
    • 7.10.3. SECURITY
  • 7.11. CARRIER CHALLENGES
    • 7.11.1. BUSINESS-CLASS SERVICES
    • 7.11.2. STANDARDIZATION
    • 7.11.3. PORTABILITY
  • 7.12. CLOUD BACK UP SERVICES FOR TELECOM
    • 7.12.1. CTERA AND TELECOM ITALIA
    • 7.12.2. HUAWEI AND CHINA TELECOM
    • 7.12.3. HUAWEI PUBLIC CLOUD AND TELKOMSIGMA
    • 7.12.4. HUAWEI PUBLIC CLOUD AND CHINACOMM
    • 7.12.5. HUAWEI DATA CENTER AND VERT BRAZIL
    • 7.12.6. ACRONIS
    • 7.12.7. VODAFONE
    • 7.12.8. OPENSTACK
    • 7.12.9. HP AND NOKIA
    • 7.12.10. ATOS
    • 7.12.11. VOX TELECOM
    • 7.12.12. ZAJIL TELECOM
    • 7.12.13. OSSTELCO
  • 7.13. CLOUD BACKUP SERVICE PROVIDER COMPEITION
  • 7.14. IMPACT OF ICLOUD

8.0. CONCLUSIONS AND RECOMMENDATIONS

  • 8.1. RECOMMENDATIONS
    • 8.1.1. CONTENT DELIVERY NETWORKS (CDN)
    • 8.1.2. MOBILE PERSONAL CLOUD SERVICES
    • 8.1.3. TELECOM OPERATOR

Figures

  • Figure 1: Cloud Computing Concept
  • Figure 2: Cloud Service Models
  • Figure 3: Benefit Chart of Cloud Computing
  • Figure 4: How Grid Computing Works
  • Figure 5: Cloud Computing Architecture
  • Figure 6: Server Virtualization Architecture
  • Figure 7: Mixed IT Environment
  • Figure 8: Cloud Professional B2B Service Provider Matrix
  • Figure 9: Cloud Computing Stack
  • Figure 10: Deployment Ratio of by Categories of SaaS Application
  • Figure 11: Difference between IaaS, PaaS, and SaaS
  • Figure 12: DaaS Ecosystem
  • Figure 13: Data Value Chain in DaaS Ecosystem
  • Figure 14: Data Value Chain with Value-added Enrichment
  • Figure 15: DaaS Elements
  • Figure 16: DaaS Benefits
  • Figure 17: Cloud Services and APIs
  • Figure 18: Cloud ERP vs. On-premise ERP
  • Figure 19: SCM Cloud Structure
  • Figure 20: Financial Services in the Cloud
  • Figure 21: Retail in the Cloud
  • Figure 22: Telecom Cloud Focus
  • Figure 23: Cloud Computing in Government and Defense
  • Figure 24: Office Automation in the Cloud
  • Figure 25: Cloud Burst of Big Data
  • Figure 26: Top Themes in the Cloud
  • Figure 27: Cloud Service Market Revenue 2015-2020
  • Figure 28: Private Cloud Storage Subscription Forecast 2015-2020
  • Figure 29: Datacenter Infrastructure
  • Figure 30: Virtualization of the Mobile Network
  • Figure 31: DevOps (Development Operations)
  • Figure 32: Cloud Radio Access Network (C-RAN)
  • Figure 33: How People User their Mobile Phone
  • Figure 34: Personal Content on Home Computer and Mobile Device
  • Figure 35: IDS1000 AIO
  • Figure 36: IDS1000 cluster

Tables

  • Table 1: Private Cloud B2C Service Provider Matrix
  • Table 2: Cloud Service Market Revenue by Private and Public Cloud 2015-2020
  • Table 3: Total Revenue Share by Private vs. Public Cloud Service 2015-2020
  • Table 4: Cloud Service Market Revenue by Core Segments or Models 2015-2020
  • Table 5: Total Revenue Share by SaaS vs. PaaS vs. IaaS 2015-2020
  • Table 6: Cloud Market Revenue by SaaS Segments 2015-2020
  • Table 7: Cloud SaaS Revenue Share by Segments during 2015-2020
  • Table 8: Cloud PaaS Market Revenue by Segments 2015-2020
  • Table 9: Percent of Cloud PaaS Revenue Share by Segments during 2015-2020
  • Table 10: Cloud PaaS Market Revenue by Sub-Segments 2015-2020
  • Table 11: Cloud PaaS Revenue Share by Sub-Segments during 2015-2020
  • Table 12: Cloud IaaS Market Revenue by Segments 2015-2020
  • Table 13: IaaS Cloud Service Market Revenue by Segments during 2015-2020
  • Table 14: Public Cloud Services Market Revenue by Segments 2015-2020
  • Table 15: Public Cloud Service Revenue by Segments during 2015-2020
  • Table 16: Public Cloud Mgt & Security Services Mkt Rev by Segments 2015-2020
  • Table 17: Public Cloud Mgt & Security Services Rev by Segment 2015-2020
  • Table 18: Public Cloud BPaaS Services Market Revenue by Segments 2015-2020
  • Table 19: Public Cloud BPaaS Services Rev by Segment 2015-2020
  • Table 20: Cloud Service Market Revenue by Region 2015-2020
  • Table 21: Cloud Service Market Revenue Share by Region 2015-2020
  • Table 22: Cloud Application Service Revenue by Industry Vertical 2015-2020
  • Table 23: Cloud Application Service Revenue by Industry Verticals 2015-2020
  • Table 24: Cloud Application Adoption Trend by Deployment Models 2015-2020
  • Table 25: Cloud Application Adoption Trend by Industry Verticals 2015-2020
  • Table 26: Cloud Investment in Industry Applications 2015
  • Table 27: Benefits of Cloud App Service Adoption over In-House IT Services
  • Table 28: Basic Features or Functionality of Mobile Personal Cloud Services

Global Cloud Computing: Infrastructure, Platforms, and Services 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. Overview

  • 2.1. Enterprise IT Systems Development
  • 2.2. Definition of Cloud Computing
  • 2.3. "Pay as you Go" Model
  • 2.4. Cloud Computing versus Virtualization
  • 2.5. Cloud Foundations
  • 2.6. Levels of Cloud Computing Services
    • 2.6.1. Web Applications
    • 2.6.2. Software as a Service (SAAS)
    • 2.6.3. Platform as a Service (PAAS)
    • 2.6.4. Infrastructure as a Service (IAAS)
      • 2.6.4.1. Public cloud
      • 2.6.4.2. Private cloud
      • 2.6.4.3. Hybrid cloud
    • 2.6.5. Communication as a Service (CAAS)
    • 2.6.6. Metal as a Service (MAAS)
    • 2.6.7. Anything as a Service (XAAS)

3. Hardware and Software

  • 3.1. Increasing Importance of Hardware
  • 3.2. Reduced Need for Provisioning Storage: A Server and a Switch
  • 3.3. Cloud Hardware will become a Commodity
  • 3.4. Low-power Processors and Cheaper Clouds
  • 3.5. Faster Interconnects
  • 3.6. Increasing Separation of Software and Hardware
  • 3.7. Rise of PAAS
  • 3.8. Increasing Popularity of JavaScript/HTML5 based Apps
  • 3.9. Stronger Identity Management systems
  • 3.10. Memory Solutions for Software and Hardware Systems
  • 3.11. Increasing use of Modular Software
  • 3.12. Top IAAS Vendors
  • 3.13. Top Software Vendors
  • 3.14. Data Center Providers
    • 3.14.1. Cloud Based Data Centers
      • 3.14.1.1. Google, Council Bluffs, Iowa
      • 3.14.1.2. Microsoft, Dublin, Republic of Ireland
      • 3.14.1.3. Switch Super NAP, Las Vegas, Nevada
      • 3.14.1.4. Range International Information Hub, Langfang China
  • 3.15. Cloud Service Providers
    • 3.15.1. Amazon
    • 3.15.2. Verizon
    • 3.15.3. IBM
    • 3.15.4. SalesForce.com
    • 3.15.5. CSC
    • 3.15.6. CENTURYLINK
    • 3.15.7. SAVVIS
    • 3.15.8. JOYENT
    • 3.15.9. MICROSOFT
    • 3.15.10. RACKSPACE
    • 3.15.11. FUJITSU
    • 3.15.12. HP
  • 3.16. Network Operators
    • 3.16.1. China Mobile Limited
    • 3.16.2. Vodafone Group
    • 3.16.3. Telenor Group
    • 3.16.4. America Movil
  • 3.17. Alternative Platforms for On-Premise and Hybrid Clouds
  • 3.18. Cloud Infrastructure and Services in Telecommunications
    • 3.18.1. Cloud RAN
    • 3.18.2. Mobile Consumer Cloud Services
      • 3.18.2.1. Consumer Mobility and the Cloud: Statistics and Forecasts
      • 3.18.2.2. Commercial Considerations
        • 3.18.2.2.1. What Consumers will Store in and Access from the Cloud
        • 3.18.2.2.2. What Devices Consumers will use to Access the Cloud
        • 3.18.2.2.3. Where and How Consumers will Access the Cloud
        • 3.18.2.2.4. What Companies do Consumers Identify with Cloud Services

4. Future Cloud Computing Applications

  • 4.1. Leveraging Cloud for Wearable Technology
    • 4.1.1. Wearable Technology is Poised for Significant Growth
    • 4.1.2. Cloud-based Services Provide a Platform for Growth
    • 4.1.3. Examples of Cloud-services Leveraged by Wearable technology
    • 4.1.4. Bottlenecks to Growth of Wearable Technology
  • 4.2. Increasing Acceptance in the Government Sector
    • 4.2.1. US Government's Consolidation of Datacenters to Cut Costs
      • 4.2.1.1. DOI's Movement to a Cloud Computing Environment
      • 4.2.1.2. The US Army's Datacenter Consolidation
      • 4.2.1.3. CIA's Private Cloud
    • 4.2.2. CSPs Building Capacity to Service Government Agencies
    • 4.2.3. Leverage Cloud for Citizen Services
    • 4.2.4. Leverage Cloud to Support Local Enterprises
  • 4.3. Cloud Computing for Media and Entertainment
    • 4.3.1. The Case of Netflix
    • 4.3.2. A Tool to Increase Subscription Revenues
    • 4.3.3. Cloud Services to Develop Stronger Links with Fans
    • 4.3.4. Cloud Services to Increase Revenue Generation Capacity
    • 4.3.5. Cloud Services to Reduce Datacenter Footprint
    • 4.3.6. Cloud Services for Controlling Digital Supply Chain
  • 4.4. Cloud Computing for Healthcare
    • 4.4.1. US Healthcare Regulations Mandate Cloud Adoption
    • 4.4.2. Heavy penalties for loss of PHI
    • 4.4.3. Healthcare's Wait and Watch Approach
    • 4.4.4. Cloud-based Solutions for Healthcare
    • 4.4.5. Adoption of Desktop Virtualization in Healthcare
    • 4.4.6. Cloud for Regional Wellness Services
    • 4.4.7. Cloud for Penetrating Rural Markets
  • 4.5. Cloud Computing for Telecoms
    • 4.5.1. The Cloud Expands the Telecoms Service Provider Product Portfolio
    • 4.5.2. SME are an Attractive Pocket for Telecom
      • 4.5.2.1. SME app market so far ignored by telecoms
      • 4.5.2.2. Bundled apps for SMEs
    • 4.5.3. Comcast Develops a Cloud Marketplace for SMEs
    • 4.5.4. Cloud-based Home Security and Home Monitoring systems
    • 4.5.5. More Cloud Opportunities
      • 4.5.5.1. AT&T PAAS
      • 4.5.5.2. AT&T teamed with IBM on cloud services
  • 4.6. Cloud Computing for Insurance
    • 4.6.1. Cloud-based Technology Provide Platforms to Increase Market Share
    • 4.6.2. Cloud-based Technology Expands Product Portfolio
  • 4.7. Cloud Computing for Utilities and Overall Energy Sector
    • 4.7.1. Smart Metering in Europe
    • 4.7.2. Cloud-based Smart Metering Projects
    • 4.7.3. Cloud and Data
    • 4.7.4. Cloud and Collaboration
    • 4.7.5. Cloud and Production Operations
    • 4.7.6. Cloud and Customer Engagement
  • 4.8. Cloud Computing for Pharmaceuticals
    • 4.8.1. Cloud-based Services are under-utilized by Pharma Companies
    • 4.8.2. Leverage Cloud Computing to Reduce Costs and Save Time
    • 4.8.3. Cloud-based Digital Market Platform
    • 4.8.4. Cloud-based On-demand Analytical Platforms
      • 4.8.4.1. GenomicsCloud
      • 4.8.4.2. DNAnexus
      • 4.8.4.3. CycleCloud for Life Sciences
    • 4.8.5. Shifting Non-core Operations to the Cloud
  • 4.9. Cloud Computing for Financial Services
    • 4.9.1. Small Banks find it Easier to Shift to the Cloud
    • 4.9.2. Cloud-based Payments Processing
    • 4.9.3. Cloud-based Document Management
    • 4.9.4. Bank of America's Pilot Test with the Cloud
    • 4.9.5. Cloud-based Mobile Banking
    • 4.9.6. Regulatory Barriers Restrict Wide Adoption of Cloud-based Analytics
    • 4.9.7. Cloud-based Monte Carlo Simulations
    • 4.9.8. Private Clouds

5. Recommendations for Enterprise

  • 5.1. When Cloud Computing is Not an Option
    • 5.1.1. Risk of External Dependencies
      • 5.1.1.1. Data protection tools
    • 5.1.2. Scalability Comes at a Cost
  • 5.2. Approach Cloud Computing from a Strategic Perspective
    • 5.2.1. What Enterprise Must Consider when Developing a Cloud Strategy
  • 5.3. Trade-off between Location of Datacenter and Service Portfolio
  • 5.4. Enterprises Must Have Strict Security and Compliance Levels
  • 5.5. Develop a Realistic Cost Estimate
    • 5.5.1. Allocate for Hidden costs
  • 5.6. Conduct CSP Background Checks
  • 5.7. Add Substance to the Term "Security"
    • 5.7.1. Data Security Measures
    • 5.7.2. Physical and Personnel Protections and Restrictions
    • 5.7.3. Geographic Location of Datacenter
  • 5.8. Understand Audit Options
    • 5.8.1. Existing Service Standards are Non-comprehensive
    • 5.8.2. Add "Right to Audit" Clause in the Contract
  • 5.9. Viability of Third-party Providers
  • 5.10. Formulate an Exit Strategy
    • 5.10.1. Prepare for Enterprise's Exit
    • 5.10.2. Prepare for CSP's Exit
  • 5.11. Evaluate all Applications in a Step-by-Step Approach
  • 5.12. Review the Contract in Detail
    • 5.12.1. Prepare a Service Checklist
      • 5.12.1.1. Plan for protecting your data:
      • 5.12.1.2. Incident response:
      • 5.12.1.3. Data lifecycle management:
      • 5.12.1.4. Vulnerability management:

6. Recommendations for Cloud Service Providers

  • 6.1. Work Side-by-Side with the Customer
  • 6.2. Understand your Customer
  • 6.3. Develop Marketing Plan for a Non-technical Audience
  • 6.4. Stress Heavily on Security Measures
    • 6.4.1. Customer's Perception of Security is Central to Aligning Needs with Capabilities
    • 6.4.2. Match SMEs Security Requirements
    • 6.4.3. Security Requirements vary from Customer-to-Customer
    • 6.4.4. SMEs Resist Installing Firewalls to Minimize Costs
    • 6.4.5. Demonstrate Security Measures
  • 6.5. Determine the Optimal Price
    • 6.5.1. Adjust Pricing strategy as required
    • 6.5.2. Understand Customer's Existing IT Costs
  • 6.6. Reorient Sales Team
  • 6.7. Provide Support with Integration
  • 6.8. Guidelines for Making a Sales Pitch
  • 6.9. Educating SMEs

7. Global Cloud Computing Market Forecasts 2015-2020

  • 7.1. Global Cloud Computing Market Value 2015-2020
  • 7.2. Market Value by Segment 2015-2020
    • 7.2.1. Segment Market Share 2015-2020
    • 7.2.2. Segment Growth Rate 2015-2020
    • 7.2.3. SAAS Market Value and Growth Rate 2015-2020
    • 7.2.4. IAAS Market Value and Growth Rate 2015-2020
    • 7.2.5. PAAS Market Value and Growth Rate 2015-2020
  • 7.3. Market Value by Region 2015-2020
    • 7.3.1. Region Market Share 2015-2020
    • 7.3.2. Region Growth Rate 2015-2020
    • 7.3.3. North America Market Value and Growth Rate 2015-2020
    • 7.3.4. Western Europe Market Value and Growth Rate 2015-2020
    • 7.3.5. Eastern Europe Market Value and Growth Rate 2015-2020
    • 7.3.6. Middle East and Africa Market Value and Growth Rate 2015-2020
    • 7.3.7. Asia Pacific Market Value and Growth Rate 2015-2020
    • 7.3.8. Rest-of-World Market Value and Growth Rate 2015-2020
  • 7.4. Market Value by Cloud Type (Public and Private) 2015-2020
    • 7.4.1. Market Share by Cloud Type 2015-2020
    • 7.4.2. Growth Rates by Cloud Type 2015-2020
    • 7.4.3. Public Cloud Market Value and Growth Rate 2015-2020
    • 7.4.4. Private Cloud Market Value and Growth Rate 2015-2020

8. Barriers and Challenges to Cloud Adoption

  • 8.1. Enterprises Reluctance to Change
  • 8.2. Responsibility of Data Security Externalized
    • 8.2.1. Loss of Control
    • 8.2.2. Privacy
  • 8.3. Security Concerns are Real
  • 8.4. Cyberattacks
    • 8.4.1. SMEs Operate with Severe Budget Restrictions
    • 8.4.2. Prolific use of Internet Increases Threats of Cyber-attacks
  • 8.5. Unclear Agreements
    • 8.5.1. SLAs do not Guarantee Downtime Requirements will be Met
    • 8.5.2. Secondary CSPs negotiate contracts on a contract to contract basis
    • 8.5.3. Enterprises are Entitled to Credit for downtime "On-Request"
    • 8.5.4. Varying Timeframes for Calculating Uptime
    • 8.5.5. Different Cloud Services have Different SLAs
  • 8.6. Complexity is a Deterrent
    • 8.6.1. Inherent Complexity in the Cloud computing Environment
    • 8.6.2. Integrating Enterprise Processes around the Cloud is a Complex Task
    • 8.6.3. Integration is a Big Issue with SAAS Deployment
      • 8.6.3.1. API management is a necessary task
      • 8.6.3.2. What is the best way to integrate data
  • 8.7. Lack of Cloud Interoperability
    • 8.7.1. Denial of Switching CSPs and Clouds
    • 8.7.2. CSPs Opt-out of Cloud Interoperability
    • 8.7.3. Challenges Faced when Moving Applications between Clouds
  • 8.8. Service Provider Resistance to Audits
    • 8.8.1. Industry Best Practices are Still Developing
    • 8.8.2. Resistance to Audit Signals Caution
      • 8.8.2.1. Internal quality controls of large enterprises make audits essential
      • 8.8.2.2. Audits inspire confidence among reluctant SMEs
  • 8.9. Viability of Third-party Providers
  • 8.10. Acceptance Issues
  • 8.11. No Move of Systems and Data is without Cost
  • 8.12. Lack of Integration Features in the Public Cloud results in Reduced Functionality

Figures

  • Figure 1: Cloud Computing
  • Figure 2: Cloud Computing Services
  • Figure 3: Datacenter
  • Figure 4: Top Cloud Service Providers
  • Figure 5: Virtualization of the Mobile Network
  • Figure 6: Cloud Radio Access Network (C-RAN)
  • Figure 7: How People User their Mobile Phone
  • Figure 8: Personal Content on Home Computer and Mobile Device
  • Figure 9: Wearable Technology Devices
  • Figure 10: Global Cloud Computing Market Value and Growth Rate 2015-2020
  • Figure 11: Market Value by segment 2015-2020
  • Figure 12: Market Share by Segment 2015-2020
  • Figure 13: Segment Growth Rate 2015-2020
  • Figure 14: SAAS Market Value and Growth Rate 2015-2020
  • Figure 15: IAAS market Value and Growth Rate 2015-2020
  • Figure 16: PAAS Market Value and Growth Rate 2015-2020
  • Figure 17: Market Value by Region 2015-2020
  • Figure 18: Market Share by Region 2015-2020
  • Figure 19: Region Growth Rate 2015-2020
  • Figure 20: North America Market Value and Growth Rate 2015-2020
  • Figure 21: Western Europe Market Value and Growth Rate 2015-2020
  • Figure 22: Eastern Europe Market Value and Growth Rate 2015-2020
  • Figure 23: Middle East and Africa Market Value and Growth Rate 2015-2020
  • Figure 24: Asia Pacific Market Value and Growth Rate 2015-2020
  • Figure 25: Rest of the World Value and Growth Rate 2015-2020
  • Figure 26: Market Value by Cloud Type (Public vs. Private) 2015-2020
  • Figure 27: Market Share by Cloud Type (Public vs. Private) 2015-2020
  • Figure 28: Growth Rates by Cloud Type (Public vs. Private) 2015-2020
  • Figure 29: Public Cloud Market Value and Growth Rate 2015-2020
  • Figure 30: Private Cloud Market Value and Growth Rate 2015-2020

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
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