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Communication Service Provider B2B Data Services: Structured and Big Data, Analytics, Cloud Services, Data as a Service, and Telecom APIs Product ID R-1511CSPDS | Research Report: 772 pages, published Nov 2015

Related Courses and Curricula Courses on Cloud Computing, SDN, and NFV Telecom for non-engineers Courses

Report in a Nutshell 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 report, “Communication Service Provider B2B Data Services: Structured and Big Data, Analytics, Cloud Services, Data as a Service, and Telecom APIs”, 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 our reports include time with an expert analyst who will help you link

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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 11 1.1 Executive Summary 11 1.2 Topics Covered 13 1.3 Key Findings 14 1.4 Target Audience 15 1.5 Companies Mentioned 16 2 Big Data Technology and Business Case 19 2.1 Structured vs. Unstructured Data 19 2.1.1 Structured Database Services in Telecom 20 2.1.2 Unstructured Data from Apps and Databases in Telecom 21 2.1.3 Emerging Hybrid (Structured/Unstructured) Database Services 22 2.2 Defining Big Data 25 2.3 Key Characteristics of Big Data 25 2.3.1 Volume 26 2.3.2 Variety 26 2.3.3 Velocity 26 2.3.4 Variability 26 2.3.5 Complexity 27 2.4 Capturing Data through Detection and Social Systems 27 2.4.1 Data in Social Systems 29 2.4.2 Detection and Sensors 31 2.4.3 Sensors in the Consumer Sector 33 2.4.4 Sensors in Industry 34 2.5 Big Data Technology 34





2.5.1 Hadoop 35 2.5.1.1 MapReduce 35 2.5.1.2 HDFS 35 2.5.1.3 Other Apache Projects 35 2.5.2 NoSQL 35 2.5.2.1 Hbase 36 2.5.2.2 Cassandra 36 2.5.2.3 Mongo DB 36 2.5.2.4 Riak 36 2.5.2.5 CouchDB 37 2.5.3 MPP Databases 37 2.5.4 Others and Emerging Technologies 37 2.5.4.1 Storm 37 2.5.4.2 Drill 37 2.5.4.3 Dremel 38 2.5.4.4 SAP HANA 38 2.5.4.5 Gremlin & Giraph 38 2.6 Business Drivers for Telecom Big Data and Analytics 38 2.6.1 Continued Growth of Mobile Broadband 39 2.6.2 Competition from New Types of Service Providers 40 2.6.3 New Technology Investment 40 2.6.4 Need for New KPIs 40 2.6.5 Artificial Intelligence and Machine Learning 41 2.7 Market Barriers 45 2.7.1 Privacy and Security: The 'Big' Barrier 45 2.7.2 Workforce Re-skilling and Organizational Resistance 46 2.7.3 Lack of Clear Big Data Strategies 46 2.7.4 Technical Challenges: Scalability and Maintenance 46 3 Key Big Data Investment Sectors 48 3.1 Industrial Internet and M2M 48 3.1.1 Big Data in M2M 48 3.1.2 Vertical Opportunities 48 3.2 Retail and Hospitality 48 3.2.1 Improving Accuracy of Forecasts and Stock Management 49 3.2.2 Determining Buying Patterns 49 3.2.3 Hospitality Use Cases 49 3.3 Media 49 3.3.1 Social Media 49 3.3.2 Social Gaming Analytics 50 3.3.3 Usage of Social Media Analytics by Other Verticals 50 3.4 Utilities 50 3.4.1 Analysis of Operational Data 50 3.4.2 Application Areas for the Future 50 3.5 Financial Services 51





3.5.1 Fraud Analysis & Risk Profiling 51 3.5.2 Merchant-Funded Reward Programs 51 3.5.3 Customer Segmentation 51 3.5.4 Insurance Companies 51 3.6 Healthcare and Pharmaceutical 51 3.6.1 Drug Development 52 3.6.2 Medical Data Analytics 52 3.6.3 Case Study: Identifying Heartbeat Patterns 52 3.7 Telecom Companies 52 3.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization 52 3.7.2 Speech Analytics 53 3.7.3 Other Use Cases 53 3.8 Government and Homeland Security 53 3.8.1 Developing New Applications for the Public 53 3.8.2 Tracking Crime 53 3.8.3 Intelligence Gathering 54 3.8.4 Fraud Detection and Revenue Generation 54 3.9 Other Sectors 54 3.9.1 Aviation: Air Traffic Control 54 3.9.2 Transportation and Logistics: Optimizing Fleet Usage 54 3.9.3 Sports: Real-Time Processing of Statistics 55 4 The Big Data Value Chain 56 4.1 Fragmentation in the Big Data Value Chain 56 4.2 Data Acquisitioning and Provisioning 57 4.3 Data Warehousing and Business Intelligence 57 4.4 Analytics and Virtualization 57 4.5 Actioning and Business Process Management (BPM) 58 4.6 Data Governance 58 5 Big Data in Telecom Analytics 59 5.1 Telecom Analytics Market 2015 - 2020 59 5.2 Improving Subscriber Experience 60 5.2.1 Generating a Full Spectrum View of the Subscriber 60 5.2.2 Creating Customized Experiences and Targeted Promotions 60 5.2.3 Central Big Data Repository: Key to Customer Satisfaction 60 5.2.4 Reduce Costs and Increase Market Share 61 5.3 Building Smarter Networks 61 5.3.1 Understanding Network Utilization 61 5.3.2 Improving Network Quality and Coverage 61 5.3.3 Combining Telecom Data with Public Data Sets: Real-Time Event Management 61 5.3.4 Leveraging M2M for Telecom Analytics 62 5.3.5 M2M, Deep Packet Inspection and Big Data: Identifying & Fixing Network Defects 62 5.4 Churn/Risk Reduction and New Revenue Streams 62 5.4.1 Predictive Analytics 62 5.4.2 Identifying Fraud and Bandwidth Theft 63





5.4.3 Creating New Revenue Streams 63 5.5 Telecom Analytics Case Studies 63 5.5.1 T-Mobile USA: Churn Reduction by 50% 63 5.5.2 Vodafone: Using Telco Analytics to Enable Navigation 64 5.6 Carriers, Analytics, and Data as a Service (DaaS) 64 5.6.1 Carrier Data Management Operational Strategies 65 5.6.2 Network vs. Subscriber Analytics 65 5.6.3 Data and Analytics Opportunities to Third Parties 66 5.6.4 Carriers to offer Data as s Service (DaaS) on B2B Basis 67 5.6.5 DaaS Planning and Strategies 67 5.6.6 Carrier Monetization of Data with DaaS 71 5.7 Opportunities for Carriers in Cloud Analytics 73 5.7.1 Carrier NFV and Cloud Analytics 73 5.7.2 Carrier Cloud OSS/BSS Analytics 73 5.7.3 Carrier Cloud Services, Data, and Analytics 74 5.7.4 Carrier Performance Management and the Cloud Analytics 75 6 Structured Data in Telecom Analytics 77 6.1 Telecom Data Sources and Repositories 77 6.1.1 Subscriber Data 77 6.1.2 Subscriber Presence and Location Data 78 6.1.3 Business Data: Toll-free and other Directory Services 82 6.1.4 Network Data: Deriving Data from Network Operations 83 6.2 Telecom Data Mining 85 6.2.1 Data Sources: Rating, Charging, and Billing Examples 86 6.2.2 Privacy Issues 87 6.3 Telecom Database Services 88 6.3.1 Calling Name Identity 88 6.3.2 Subscriber Data Management (SDM) Services 93 6.3.3 Other Data-intensive Service Areas 95 6.3.4 Emerging Service Area: Identity Verification 96 6.4 Structured Telecom Data Analytics 96 6.4.1 Dealing with Telecom Data Fragmentation 97 6.4.2 Deep Packet Inspection 99 7 Key Players in the Big Data Market 103 7.1 Vendor Assessment Matrix 103 7.2 Apache Software Foundation 103 7.3 Accenture 104 7.4 Amazon 104 7.5 APTEAN (Formerly CDC Software) 104 7.6 Cisco Systems 105 7.7 Cloudera 105 7.8 Dell 105 7.9 EMC 105 7.10 Facebook 106





7.11 GoodData Corporation 106 7.12 Google 106 7.13 Guavus 107 7.14 Hitachi Data Systems 107 7.15 Hortonworks 107 7.16 HP 108 7.17 IBM 108 7.18 Informatica 108 7.19 Intel 108 7.20 Jaspersoft 109 7.21 Microsoft 109 7.22 MongoDB (Formerly 10Gen) 109 7.23 MU Sigma 110 7.24 Netapp 110 7.25 Opera Solutions 110 7.26 Oracle 111 7.27 ParStream 111 7.28 Pentaho 111 7.29 Platfora 111 7.30 Qliktech 112 7.31 Quantum 112 7.32 Rackspace 112 7.33 Revolution Analytics 112 7.34 Salesforce 113 7.35 SAP 113 7.36 SAS Institute 114 7.37 Sisense 114 7.38 Software AG/Terracotta 114 7.39 Splunk 115 7.40 Sqrrl 115 7.41 Supermicro 115 7.42 Tableau Software 116 7.43 Teradata 116 7.44 Think Big Analytics 116 7.45 Tidemark Systems 116 7.46 VMware (Part of EMC) 117 8 Market Analysis 118 8.1 Market for Structured Telecom Data Services 118 8.2 Market for Unstructured (Big) Data Services 122 8.2.1 Big Data Revenue 2015 - 2020 122 8.2.2 Big Data Revenue by Functional Area 2015 - 2020 123 8.2.3 Big Data Revenue by Region 2015 - 2020 124 9 Summary and Recommendations 126 9.1 Key Success Factors for Carriers 127





9.1.1 Leverage Real-time Data 127 9.1.2 Recognize that Analytics is Not Business Intelligence 128 9.1.3 Provide Data Discovery Services 130 9.1.4 Provide Big Data and Analytics to Enterprise Customers 133 9.2 The Role of Intermediaries in the Ecosystem 133 9.2.1 Cloud and Big Data Intermediation 134 9.2.2 Security, Communications, Billing, and Settlement 135 9.2.3 The Case for Data as a Service (DaaS) 137 10 Appendix: Understanding Big Data Analytics 142 10.1 What is Big Data Analytics? 142 10.2 The Importance of Big Data Analytics 143 10.3 Reactive vs. Proactive Analytics 144 10.4 Technology and Implementation Approaches 146 10.4.1 Grid Computing 146 10.4.2 In-Database processing 146 10.4.3 In-Memory Analytics 149 10.4.4 Data Mining 149 10.4.5 Predictive Analytics 151 10.4.6 Natural Language Processing 153 10.4.7 Text Analytics 157 10.4.8 Visual Analytics 158 10.4.9 Association Rule Learning 159 10.4.10 Classification Tree Analysis 160 10.4.11 Machine Learning 160 10.4.11.1 Neural Networks 162 10.4.11.2 Multilayer Perceptron (MLP) 163 10.4.11.3 Radial Basis Functions 165 10.4.11.4 Support Vector Machines 165 10.4.11.5 Naïve Bayes 165 10.4.11.6 k-nearest Neighbours 166 10.4.11.7 Geospatial Predictive Modelling 167 10.4.12 Regression Analysis 167 10.4.13 Social Network Analysis 168 Figures Figure 1: Hybrid Data in Next Generation Applications 24 Figure 2: Big Data Components 25 Figure 3: Big Data Sources 28 Figure 4: Capturing Data from Detection Systems and Sensors 32 Figure 5: Capturing Data across Sectors 33 Figure 6: AI Structure 42 Figure 7: The Big Data Value Chain 56 Figure 8: Telco Analytics Investments Driven by Big Data: 2015 - 2020 59





Figure 9: Different Data Types within Telco Environment 69 Figure 10: Presence-enabled Application 81 Figure 11: Calling Name (CNAM) Service Operation 89 Figure 12: Subscriber Data Management (SDM) Ecosystem 94 Figure 13: Data Fragmented across Telecom Databases 98 Figure 14: Telecom Deep Packet Inspection Revenue 2015 - 2020 102 Figure 15: Big Data Vendor Ranking Matrix 103 Figure 16: Unified Communications Incoming Call Routing 120 Figure 17: Network Level Outbound Call Management 121 Figure 18: Big Data Revenue: 2015 - 2020 123 Figure 19: Big Data Revenue by Functional Area: 2015 - 2020 124 Figure 20: Big Data Revenue by Region: 2015 - 2020 125 Figure 21: Data Mediation for Structured and Unstructured Data 134 Figure 21: Cloud and Big Data Intermediation 135 Figure 22: Data Security, Billing and Settlement 137 Figure 24: Big Data as a Service (BDaaS) 139 Telecom API Marketplace: Strategy, Ecosystem, Players and Forecasts 2015 - 2020 1 Introduction 11 1.1 Executive Summary 11 1.2 Topics Covered 12 1.3 Key Findings 14 1.4 Target Audience 15 1.5 Companies Mentioned 16 2 Telecom Network API Overview 18 2.1 Defining Network APIs 18 2.2 Why Carriers are Adopting Telecom Network APIs 19 2.2.1 Need for New Revenue Sources 20 2.2.2 B2B Services and Asymmetric Business Models 21 2.3 Telecom Network API Categories 22 2.3.1 Web Real-time Communications (WebRTC) 22 2.3.2 SMS and RCS-E 24 2.3.3 Presence 24 2.3.4 MMS 24 2.3.5 Location 24 2.3.6 Payments 25 2.3.7 Voice/Speech 25 2.3.8 Voice Control 25 2.3.9 Multimedia Voice Control 25 2.3.10 M2M 25 2.3.11 SDM/Identity Management 25 2.3.12 Subscriber Profile 26 2.3.13 QoS 26





2.3.14 ID/SSO 26 2.3.15 Content Delivery 26 2.3.16 Hosted UC 26 2.3.17 Directory 26 2.3.18 Number Provisioning 26 2.3.19 USSD 27 2.3.20 Billing of Non-Digital Goods 27 2.3.21 Advertising 27 2.3.22 Collaboration 27 2.3.23 IVR/Voice Store 27 2.4 Telecom Network API Business Models 28 2.4.1 Two-Sided Business Model 28 2.4.2 Exposing APIs to Developers 28 2.4.3 Web Mash-ups 29 2.5 Segmentation 29 2.5.1 Users by Segment 29 2.5.2 Workforce Management 29 2.6 Competitive Issues 30 2.6.1 Reduced TCO 30 2.6.2 Open APIs 30 2.6.3 Configurability 30 2.7 Percentage of Applications that use APIs 31 2.8 Telecom API Revenue Potential 32 2.8.1 Standalone API Revenue vs. Finished Goods Revenue 33 2.8.2 Telecom API-enabled Mobile VAS Applications 35 2.8.3 Carrier Focus on Telecom API's for the Enterprise 38 2.9 Telecom Network API Usage by Industry Segment 44 2.10 Telecom Network API Value Chain 44 2.10.1 Telecom API Value Chain 44 2.10.2 How the Value Chain Evolve 46 2.10.3 API Transaction Value Split among Players 46 2.11 Cost for Different API Transactions 48 2.12 Volume of API Transactions 48 3 API Aggregation 50 3.1 The Role of API Aggregators 50 3.2 Total Cost Usage for APIs with Aggregators 50 3.2.1 Start-up Costs 51 3.2.2 Transaction Costs 51 3.2.3 Ongoing Maintenance/Support 51 3.2.4 Professional Services by Intermediaries 51 3.3 Aggregator API Usage by Category 52 3.3.1 An LBS Case Study: LOC-AID 52 3.3.2 Aggregation: Intersection of Two Big Needs 52 3.3.3 The Case for Other API Categories 53





3.3.4 Moving Towards New Business Models 54 4 Enterprise and Telecom API Marketplace 55 4.1 Data as a Service (DaaS) 55 4.1.1 Carrier Structured and Unstructured Data 58 4.1.2 Carrier Data Management in DaaS 62 4.1.3 Data Federation in the DaaS Ecosystem 62 4.2 API Market Makers 64 4.2.1 mashape 64 4.2.2 Mulesoft 65 4.3 Need for a New Type of Application Marketplace: CAM 66 4.3.1 Communications-enabled App Marketplace (CAM) 66 4.3.2 CAM Market Opportunities and Challenges 67 5 Telecom API Enabled App Use Cases 69 5.1 Monetization of Communications-enabled Apps 69 5.1.1 Direct API Revenue 69 5.1.2 Data Monetization 70 5.1.3 Cost Savings 70 5.1.4 Higher Usage 70 5.1.5 Churn Reduction 70 5.2 Use Case Issues 71 5.2.1 Security 71 5.2.2 Interoperability 71 6 Non-Telecom Network APIs and Mash-ups 73 6.1 Non-Telecom Network APIs 73 6.1.1 Twitter 73 6.1.2 Netflix API 74 6.1.3 Google Maps 74 6.1.4 Facebook 75 6.1.5 YouTube 75 6.1.6 Flickr 76 6.1.7 eBay 76 6.1.8 Last.fm 77 6.1.9 Amazon Web Services 77 6.1.10 Bing Maps 78 6.1.11 Yahoo Web Search API 78 6.1.12 Shopping.com 79 6.1.13 Salesforce.com 80 6.2 Mash-ups 81 6.2.1 BBC News on Mobile 81 6.2.2 GenSMS emailSMS 81 6.2.3 Foursquare 82 6.2.4 Amazon SNS and Nexmo 82 6.2.5 Triage.me 83 6.2.6 MappyHealth 83





6.2.7 Lunchflock 84 6.2.8 Mobile Time Tracking 84 6.2.9 Fitsquare 84 6.2.10 GeoSMS 84 6.2.11 FONFinder 85 6.2.12 Pound Docs 85 6.2.13 140Call 85 6.2.14 Salesforce SMS 85 7 Carrier Strategies 86 7.1 Carrier Market Strategy and Positioning 86 7.1.1 Increasing API Investments 86 7.1.2 The Rise of SDM 86 7.1.3 Telecom API Standardization 87 7.1.4 Carrier Attitudes towards APIs: U.S vs. Asia Pacific and Western Europe 89 7.2 Carrier API Programs Worldwide 89 7.2.1 AT&T Mobility 89 7.2.2 Verizon Wireless 93 7.2.3 Vodafone 95 7.2.4 France Telecom 95 7.2.5 Telefonica 97 7.3 Carriers and Internal Telecom API Usage 99 7.3.1 The Case for Internal Usage 99 7.3.2 Internal Telecom API Use Cases 100 7.4 Carriers and OTT Service Providers 101 7.4.1 Allowing OTT Providers to Manage Applications 101 7.4.2 Carriers Lack the Innovative Skills to Capitalize on APIs Alone 101 7.5 Carriers and Value-added Services (VAS) 102 7.5.1 The Role and Importance of VAS 102 7.5.2 The Case for Carrier Communication-enabled VAS 103 7.5.3 Challenges and Opportunities for Carriers in VAS 104 8 API enabled App Developer Strategies 106 8.1 A Critical Asset to Developers 106 8.2 Stimulating the Growth of API Releases 106 8.3 Working alongside Carrier Programs 106 8.4 Developer Preferences: Google vs Carriers 107 9 Telecom API Vendor Strategies 108 9.1 Positioning as Enablers in the Value Chain 108 9.2 Moving Away from a Box/Product Supplier Strategy 108 9.3 Telecom API Companies and Solutions 109 9.3.1 Alcatel Lucent 109 9.3.2 UnboundID 110 9.3.3 Twilio 112 9.3.4 LOC-AID 113 9.3.5 Placecast 114





9.3.6 Samsung 117 9.3.7 AT&T Mobility 118 9.3.8 Apigee 118 9.3.9 2600 Hz 120 9.3.10 Callfire 120 9.3.11 Plivo 121 9.3.12 Tropo (now part of Cisco) 122 9.3.13 Urban Airship 122 9.3.14 Voxeo (now Aspect Software) 123 9.3.15 TeleStax 123 9.3.16 Intel 124 9.3.17 Competitive Differentiation 126 10 Market Analysis and Forecasts 127 10.1 Telecom Network API Revenue 2015 - 2020 127 10.2 Telecom Network APIs Revenue by API Category 2015 - 2020 128 10.2.1 Messaging API Revenues 129 10.2.2 LBS API Revenues 130 10.2.3 SDM API Revenues 131 10.2.4 Payment API Revenues 132 10.2.5 Internet of Things (IoT) API Revenues 133 10.2.6 Other API Revenues 134 10.3 Telecom API Revenue by Region 2015 - 2020 135 10.3.1 Asia Pacific 136 10.3.2 Eastern Europe 137 10.3.3 Latin & Central America 138 10.3.4 Middle East & Africa 139 10.3.5 North America 140 10.3.6 Western Europe 141 11 Technology and Market Drivers for Future API Market Growth 142 11.1 Service Oriented Architecture (SOA) 142 11.2 Software Defined Networks (SDN) 145 11.3 Virtualization 146 11.3.1 Network Function Virtualization (NFV) 148 11.3.2 Virtualization beyond Network Functions 151 11.4 The Internet of Things (IoT) 152 11.4.1 IoT Definition 152 11.4.2 IoT Technologies 155 11.4.3 IoT Applications 171 11.4.4 IoT Solutions 172 11.4.5 IoT, DaaS, and APIs (Telecom and Enterprise) 172 12 Expert Opinion: TeleStax 178 13 Expert Opinion: Twilio 183 14 Expert Opinion: Point.io 189 15 Expert Opinion: Nexmo 192





16 Appendix 195 16.1 Research Methodology 195 16.2 Telecom API Definitions 197 16.3 More on Telecom APIs and DaaS 201 16.3.1 Tiered Data Focus 202 16.3.2 Value-based Pricing 204 16.3.3 Open Development Environment 205 16.3.4 Specific Strategies 206 16.3.4.1 Service Ecosystem and Platforms 206 16.3.4.2 Bringing to Together Multiple Sources for Mash-ups 207 16.3.4.3 Developing Value-added Services (VAS) as Proof Points 208 16.3.4.4 Open Access to all Entities including Competitors 208 16.3.4.5 Prepare for Big Opportunities with the Internet of Things (IoT) 209 Figures Figure 1: Wireless Carrier Assets 19 Figure 2: Telecom API: Standalone vs. Finished Services 33 Figure 3: RCS and Telecom API Integration 36 Figure 4: RCS Revenue Forecast 37 Figure 5: Business vs. Consumer Telecom API Focus 40 Figure 6: Enterprise Dashboard 41 Figure 7: Enterprise Dashboard App Example 42 Figure 8: Telecom Network API Value Chain 45 Figure 9: Value Split among Aggregators, Carriers and Enterprise for API Transactions: 2012 - 2019 47 Figure 10: API Transaction Costs (US Cents) 2012 - 2019 48 Figure 11: Volume of API Transactions for a Tier 1 Carrier (Billions per Month): 2015 - 2020 49 Figure 12: Cloud Services and APIs 58 Figure 13: GSMA OneAPI: Benefits to Stakeholders 88 Figure 14: AT&T Wireless API Catalog 90 Figure 15: Verizon Wireless API Program 93 Figure 16: France Telecom (Orange) APIs 96 Figure 17: Telefonica APIs 98 Figure 18: Carrier Internal Use of Telecom APIs 100 Figure 19: UnboundID's Portfolio of Services 111 Figure 20: Twilio's Portfolio of Services 112 Figure 21: LOC-AID Exchange Server Architecture 113 Figure 22: Placecast's ShopAlerts Solution 115 Figure 23: Apigee Portfolio of Services 119 Figure 24: Telecom API Revenue (USD Billions) 2015 - 2020 127 Figure 25: Telecom API Revenue (USD Billions) by API Category 2015 - 2020 128 Figure 26: Messaging APIs Revenue (USD Billions) 2015 - 2020 129 Figure 27: LBS APIs Revenue (USD Billions) 2015 - 2020 130





Figure 28: SDM APIs Revenue (USD Billions) 2015 - 2020 131 Figure 29: Payment APIs Revenue (USD Billions) 2015 - 2020 132 Figure 30: IoT API Revenue (USD Billions) 2015 - 2020 133 Figure 31: APIs Revenue for Other Categories (USD Billions) 2015 - 2020 134 Figure 32: Telecom API Revenue (USD Billions) by Region 2015 - 2020 135 Figure 33: Telecom API Revenue (USD Billions) Asia Pacific 2015 - 2020 136 Figure 34: Telecom API Revenue (USD Billions) Eastern Europe 2015 - 2020 137 Figure 35: Telecom API Revenue (USD Billions) Latin & Central America 2015 - 2020 138 Figure 36: Telecom API Revenue (USD Billions) Middle East & Africa 2015 - 2020 139 Figure 37: Telecom API Revenue (USD Billions) North America 2015 - 2020 140 Figure 38: Telecom API Revenue (USD Billions) Western Europe 2015 - 2020 141 Figure 39: Services Oriented Architecture 143 Figure 40: Growth of Connected Devices 159 Figure 41: IoT and Telecom API Topology 176 Figure 42: Telestax App Store Funnel 180 Figure 43: On-Premise vs. Twilio 188 Figure 44: Point.io and API Ecosystem 189 Figure 45: Different Data Types and Functions in DaaS 203 Figure 46: Ecosystem and Platform Model 206 Figure 47: Telecom API and Internet of Things Mediation 210 Figure 48: DaaS and IoT Mediation for Smartgrid 212 Cloud Application Marketplace 2015 - 2020 1.0 EXECUTIVE SUMMARY 10 2.0 OVERVIEW OF CLOUD COMPUTING 13 2.1 UNDERSTANDING CLOUD COMPUTING 13 2.1.1 CLOUD COMPUTING SERVICES 14 2.2 CLOUD FOUNDATIONS 15 2.2.1 CATEGORIES OF CLOUD COMPUTING DEPLOYMENT MODEL 16 2.2.2 GRID COMPUTING 17 2.2.3 GRID COMPUTING MARKET SEGMENTATION 18 2.3 CLOUD TECHNOLOGIES AND ARCHITECTURE 18 2.3.1 SOFTWARE DEFINED NETWORKING (SDN) 20 2.3.2 SDN DEPLOYMENT MODELS 21 2.3.3 VIRTUALIZATION (SERVER VS. HARDWARE VS. DESKTOP VS. STORAGE) 22 2.4 CLOUD COMPUTING AND VIRTUALIZATION 24 2.5 MOVING BEYOND CLOUD COMPUTING 25 2.5.1 A 'GLOCAL' CLOUD 27 2.6 RISE OF THE CLOUD-BASED NETWORKED ENTERPRISE 28 2.7 GENERAL CLOUD SERVICE ENABLERS 28 2.7.1 WIRELESS BROADBAND CONNECTIVITY 28 2.7.2 SECURITY SOLUTIONS 29 2.7.3 PRESENCE AND LOCATION 29





2.8 PERSONAL CLOUD SERVICE ENABLERS 30 2.8.1 IDENTITY MANAGEMENT 30 2.8.2 PREFERENCE MANAGEMENT 31 3.0 CLOUD SERVICE ANALYSIS 32 3.1 CLOUD SERVICE SEGMENTATION 32 3.1.1 BUSINESS TO BUSINESS (B2B) 32 3.1.2 BUSINESS TO CONSUMER (B2C) 33 3.2 CORE CLOUD SERVICES 34 3.2.1 INFRASTRUCTURE AS A SERVICE (IAAS) 35 3.2.2 PLATFORM AS A SERVICE (PAAS) 36 3.2.3 SOFTWARE AS A SERVICE (SAAS) 38 3.2.4 DIFFERENCES BETWEEN IAAS, SAAS, AND PAAS 40 3.3 EMERGING MODELS: XAAS (EVERYTHING AS A SERVICE) 41 3.3.1 BUSINESS PROCESS AS A SERVICE (BPAAS) 41 3.3.2 COMMUNICATION AS A SERVICE (CAAS) 42 3.3.3 MONITORING AS A SERVICE (MAAS) 42 3.3.4 NETWORK-AS-A-SERVICE (NAAS) 42 3.3.5 STORAGE AS A SERVICE (SAAS) 43 3.3.6 DATA AS A SERVICE (DAAS) 43 3.4 DATA AS A SERVICE ECOSYSTEM 44 3.4.1 THE DRIVERS OF DATA-AS-A-SERVICE 44 3.4.2 BUSINESS INTELLIGENCE AND DAAS INTEGRATION 45 3.4.3 THE CLOUD ENABLER DAAS 47 3.4.4 XAAS DRIVES DAAS 47 3.4.5 THE DAAS ECOSYSTEM 47 3.4.6 DAAS ELEMENTS 53 3.4.7 THE ROLE OF DATA MARTS 54 3.4.8 BEST PRACTICES IN DAAS 54 3.4.9 BENEFITS OF DAAS 55 3.4.10 CHALLENGES OF DATA AS A SERVICE 56 3.4.11 APIS AND DATABASE 58 3.4.12 THE NEED FOR FEDERATED DATABASE MODEL 59 3.5 ENTERPRISE RESOURCE PLANNING IN THE CLOUD 60 3.6 SUPPLY CHAIN MANAGEMENT IN THE CLOUD 62 4.0 INDUSTRY VERTICALS IN THE CLOUD 65 4.1 FINANCE AND BANKING IN THE CLOUD 65 4.1.1 AGILITY, EFFICIENCY, AND SIMPLIFIED DELIVERY 66 4.1.2 PRIORITIZING THE CLOUD 66 4.2 RETAIL IN THE CLOUD 67 4.3 HEALTHCARE IN THE CLOUD 69 4.3.1 KEY BENEFITS OF CLOUD TECHNOLOGY 70 4.4 TELECOMMUNICATIONS IN THE CLOUD 71 4.4.1 OPPORTUNITIES AND CHALLENGES 72 4.4.2 SOLUTIONS 72





4.5 GOVERNMENT AND DEFENSE IN THE CLOUD 73 4.5.1 PROS AND CONS OF THE FEDERAL CLOUD 74 4.6 WORKFORCE IN THE CLOUD 75 4.6.1 HUMAN CAPITAL MANAGEMENT IN THE CLOUD 75 4.6.2 TRAINING AND EDUCATION IN THE CLOUD 76 4.6.3 COLLABORATION IN THE CLOUD 77 4.6.4 OFFICE AUTOMATION IN THE CLOUD 78 4.7 CUSTOMERS IN THE CLOUD 79 4.7.1 CUSTOMER RELATIONSHIP IN THE CLOUD 79 4.7.2 COMMERCE AND PAYMENTS IN THE CLOUD 79 4.8 EMERGING CLOUD BASED APPLICATIONS 80 4.8.1 B2B APPLICATIONS 80 4.8.2 BIG DATA AS A SERVICE (BDAAS) 82 4.8.3 B2C APPLICATIONS 83 4.8.4 ENTERTAINMENT IN THE CLOUD: TV, VIDEO, GAMING AND MORE 84 4.9 THE FUTURE OF CLOUD SERVICES 85 4.9.1 EVERYTHING AS A SERVICE 86 4.9.2 HOW XAAS DECREASES COSTS AND MAKES EVERYTHING FIT TOGETHER 88 4.10 DATA CENTER PROVIDERS 89 4.11 VIRTUALIZATION: ROLE AND IMPACT 89 4.11.1 TYPES OF VIRTUALIZATION 90 4.11.2 HOW VIRTUALIZATION AFFECTS COST STRUCTURES 90 5.0 CLOUD APPLICATION SERVICE MARKET FORECAST 92 5.1 CLOUD SERVICE MARKET REVENUE FORECAST 2015 - 2020 92 5.2 CLOUD SERVICE MARKET REVENUE BY TPES 2015 - 2020 92 5.3 CLOUD SERVICE MARKET REVENUE BY CORE SEGMENTS OR MODELS 2015 - 2020 93 5.4 CLOUD SAAS MARKET REVENUE BY SEGMENTS 2015 - 2020 93 5.5 CLOUD PAAS MARKET REVENUE BY SEGMENTS 2015 - 2020 94 5.5.1 CLOUD PAAS MARKET REVENUE BY SUB-SEGMENTS 2015 - 2020 94 5.6 CLOUD IAAS MARKET REVENUE BY SEGMENTS 2015 - 2020 95 5.7 PUBLIC CLOUD SERVICES MARKET REVENUE BY SEGMENTS 2015 - 2020 95 5.7.1 PUBLIC CLOUD MANAGEMENT & SECURITY SERVICES MARKET REVENUE BY SEGMENTS 2015 - 2020 96 5.7.2 PUBLIC CLOUD BPAAS SERVICES MARKET REVENUE BY SEGMENTS 2015 - 2020 96 5.8 CLOUD SERVICE MARKET REVENUE BY GEOGRAPHIC REGION 2015 - 2020 97 5.9 CLOUD APPLICATION SERVICE REVENUE BY INDUSTRY VERTICAL 2015 - 2020 98 5.10 CLOUD APPLICATION ADOPTION TREND AMONG PERCENT OF ORGANIZATIONS BY DEPLOYMENT MODELS 2015 - 2020 98 5.11 CLOUD APPLICATION ADOPTION TREND AMONG PERCENT OF ORGANIZATIONS BY INDUSTRY VERTICALS 2015 - 2020 99 5.12 CLOUD INVESTMENT PERCENT TO INDUSTRY APPLICATIONS 2015 99 5.13 BENEFITS OF CLOUD APPLICATION SERVICE ADOPTION OVER IN-HOUSE IT SERVICES 100 5.14 PRIVATE CLOUD STORAGE SUBSCRIPTION FORECAST 2015 - 2020 100





6.0 CLOUD APPLICATION SERVICE VENDOR ANALYSIS 101 6.1 OFFICE AUTOMATION APPLICATION 101 6.1.1 ZOHO 101 6.1.2 TECHINLINE 101 6.1.3 WINDOWS LIVE MESH 101 6.1.4 DROPBOX 101 6.1.5 LOGMEIN 101 6.1.6 MICROSOFT OFFICE 365 101 6.1.7 NOODLE 101 6.2 CRM APPLICATIONS 102 6.2.1 ADDRESSTWO 102 6.2.2 ALLCLIENTS 102 6.2.3 MAXIMIZER 102 6.2.4 SALESCLOUD FROM SALESFORCE 102 6.2.5 SALESNEXUS 103 6.3 DATA CENTER APPLICATIONS 103 6.3.1 GOOGLE 103 6.3.2 MICROSOFT 104 6.3.3 SWITCH SUPER NAP 104 6.3.4 RANGE INTERNATIONAL INFORMATION HUB 105 6.4 CORE CLOUD SERVICE PROVIDERS 105 6.4.1 AMAZON 105 6.4.2 VERIZON 105 6.4.3 IBM 106 6.4.4 SALESFORCE.COM 106 6.4.5 CSC 106 6.4.6 CENTURYLINK 107 6.4.7 SAVVIS 107 6.4.8 JOYENT 107 6.4.9 MICROSOFT 107 6.4.10 RACKSPACE 107 6.4.11 FUJITSU 107 6.4.12 HP 108 6.5 CLOUD NETWORK OPERATORS 108 6.5.1 CHINA MOBILE LIMITED 108 6.5.2 VODAFONE GROUP 108 6.5.3 TELENOR GROUP 109 6.5.4 AMERICA MOVIL 109 6.6 ENTERPRISE CLOUD APPLICATION 110 6.6.1 SALESFORCE.COM 111 6.6.2 BOX 111 6.6.3 CRASHPLAN 111 6.6.4 AMAZON WEB SERVICES 111 6.6.5 EASY VISTA 111





7.0 CARRIER CLOUD OPPORTUNITY 112 7.1 CLOUD INFRASTRUCTURE AND SERVICES IN TELECOMMUNICATIONS 112 7.1.1 CLOUD RAN 112 7.2 MOBILE CONSUMER CLOUD SERVICES 113 7.2.1 CONSUMER MOBILITY AND THE CLOUD: STATISTICS AND FORECASTS 115 7.3 COMMERCIAL CONSIDERATIONS 116 7.3.1 WHAT CONSUMERS WILL STORE IN AND ACCESS FROM THE CLOUD 116 7.3.2 WHAT DEVICES CONSUMERS WILL USE TO ACCESS THE CLOUD 116 7.3.3 WHERE AND HOW CONSUMERS WILL ACCESS THE CLOUD 117 7.3.4 WHAT COMPANIES DO CONSUMERS IDENTIFY WITH CLOUD SERVICES 117 7.3.5 CONSUMER WILLINGNESS TO PAY FOR PERSONAL CLOUD SERVICES 117 7.4 KEY CONCERNS AND SOLUTIONS FOR PERSONAL CLOUD SERVICES 118 7.4.1 LTE AND ANYWHERE, ANYTIME, ANY DEVICE ACCESS 118 7.4.2 LTE DRIVES CLOUD GROW ACCELERATION VIA USER GENERATED CONTENT (UGC) 119 7.4.3 DIGITAL RIGHTS MANAGEMENT (DRM) 120 7.4.4 NETWORK AND DEVICE OPTIMIZATION 120 7.4.5 CLOUD DATA SECURITY 120 7.4.6 IDENTITY MANAGEMENT FOR CLOUD SERVICES 120 7.4.7 CLOUD SERVICES BROKERING AND CLOUD MEDIATION 121 7.5 MOBILE NETWORK OPERATOR VAS APPLICATION VS. OTT APPLICATIONS 121 7.6 TELECOM APIS AND THE CLOUD 123 7.6.1 ROLE OF API'S IN THE CLOUD 124 7.6.2 ENTERPRISE API PROVIDERS AND CLOUD SERVICES 124 7.6.3 TELECOM API'S AND THE CLOUD 125 7.7 GREATER MOBILE CLOUD COMPUTING 126 7.7.1 BYOC (BRING YOUR OWN CLOUD) AND INCREASED SECURITY 126 7.8 CARRIER CLOUD SERVICE STRATEGY 126 7.8.1 CONSUMER CLOUD SERVICES KEY TO GROWTH IN CARRIER DATA SERVICES 127 7.8.2 CARRIER CLOUD SERVICES TO DRIVE VALUE-ADDED SERVICES GROWTH 127 7.8.3 PERSONAL CLOUD SERVICES TO IMPROVE CARRIER TOP LINE REVENUE AND PROFITS 128 7.9 TELECOM BENEFITS OF OFFERING CLOUD SERVICES 128 7.9.1 VALUE PROPOSITION FOR TELECOM 129 7.9.2 WEB-BASED APPLICATIONS PROMOTE IT INDEPENDENCE 129 7.9.3 CLOUD-BASED MANAGED SERVICES PRODUCES REVENUE 129 7.9.4 INCREASE DATA CENTER EFFICIENCY AND OPERATIONS 130 7.9.5 DIFFERENTIATING SERVICE PROVIDERS 130 7.10 CARRIER ADVANTAGES IN CLOUD ECOSYSTEM 131 7.10.1 SERVICE-ORIENTATION 131 7.10.2 PERFORMANCE 131 7.10.3 SECURITY 131 7.11 CARRIER CHALLENGES 131 7.11.1 BUSINESS-CLASS SERVICES 132





7.11.2 STANDARDIZATION 132 7.11.3 PORTABILITY 132 7.12 CLOUD BACK UP SERVICES FOR TELECOM 132 7.12.1 CTERA AND TELECOM ITALIA 132 7.12.2 HUAWEI AND CHINA TELECOM 133 7.12.3 HUAWEI PUBLIC CLOUD AND TELKOMSIGMA 136 7.12.4 HUAWEI PUBLIC CLOUD AND CHINACOMM 136 7.12.5 HUAWEI DATA CENTER AND VERT BRAZIL 137 7.12.6 ACRONIS 138 7.12.7 VODAFONE 138 7.12.8 OPENSTACK 138 7.12.9 HP AND NOKIA 139 7.12.10 ATOS 140 7.12.11 VOX TELECOM 141 7.12.12 ZAJIL TELECOM 141 7.12.13 OSSTELCO 142 7.13 CLOUD BACKUP SERVICE PROVIDER COMPEITION 143 7.14 IMPACT OF ICLOUD 144 8.0 CONCLUSIONS AND RECOMMENDATIONS 146 8.1 RECOMMENDATIONS 146 8.1.1 CONTENT DELIVERY NETWORKS (CDN) 148 8.1.2 MOBILE PERSONAL CLOUD SERVICES 149 8.1.3 TELECOM OPERATOR 154 Figures Figure 1: Cloud Computing Concept 13 Figure 2: Cloud Service Models 15 Figure 3: Benefit Chart of Cloud Computing 15 Figure 4: How Grid Computing Works 17 Figure 5: Cloud Computing Architecture 20 Figure 6: Server Virtualization Architecture 23 Figure 7: Mixed IT Environment 27 Figure 8: Cloud Professional B2B Service Provider Matrix 33 Figure 9: Cloud Computing Stack 35 Figure 10: Deployment Ratio of by Categories of SaaS Application 38 Figure 11: Difference between IaaS, PaaS, and SaaS 40 Figure 12: DaaS Ecosystem 48 Figure 13: Data Value Chain in DaaS Ecosystem 51 Figure 14: Data Value Chain with Value-added Enrichment 52 Figure 15 DaaS Elements 53 Figure 16 DaaS Benefits 56 Figure 17: Cloud Services and APIs 59 Figure 18 Cloud ERP vs. On-premise ERP 61





Figure 19: SCM Cloud Structure 62 Figure 20: Financial Services in the Cloud 66 Figure 21: Retail in the Cloud 68 Figure 22: Telecom Cloud Focus 72 Figure 23: Cloud Computing in Government and Defense 75 Figure 24: Office Automation in the Cloud 78 Figure 25: Cloud Burst of Big Data 82 Figure 26: Top Themes in the Cloud 86 Figure 27 Cloud Service Market Revenue 2015 - 2020 92 Figure 28: Private Cloud Storage Subscription Forecast 2015 - 2020 100 Figure 29: Datacenter Infrastructure 104 Figure 30: Virtualization of the Mobile Network 109 Figure 31: DevOps (Development Operations) 110 Figure 32: Cloud Radio Access Network (C-RAN) 112 Figure 33: How People User their Mobile Phone 114 Figure 34: Personal Content on Home Computer and Mobile Device 115 Figure 35: IDS1000 AIO 137 Figure 36: IDS1000 cluster 138 Tables Table 1: Private Cloud B2C Service Provider Matrix 34 Table 2: Cloud Service Market Revenue by Private and Public Cloud 2015 - 2020 92 Table 3: Total Revenue Share by Private vs. Public Cloud Service 2015 - 2020 92 Table 4: Cloud Service Market Revenue by Core Segments or Models 2015 - 2020 93 Table 5: Total Revenue Share by SaaS vs. PaaS vs. IaaS 2015 - 2020 93 Table 6: Cloud Market Revenue by SaaS Segments 2015 - 2020 93 Table 7: Cloud SaaS Revenue Share by Segments during 2015 - 2020 93 Table 8: Cloud PaaS Market Revenue by Segments 2015 - 2020 94 Table 9: Percent of Cloud PaaS Revenue Share by Segments during 2015 - 2020 94 Table 10 Cloud PaaS Market Revenue by Sub-Segments 2015 - 2020 94 Table 11: Cloud PaaS Revenue Share by Sub-Segments during 2015 - 2020 95 Table 12: Cloud IaaS Market Revenue by Segments 2015 - 2020 95 Table 13: IaaS Cloud Service Market Revenue by Segments during 2015 - 2020 95 Table 14: Public Cloud Services Market Revenue by Segments 2015 - 2020 95 Table 15: Public Cloud Service Revenue by Segments during 2015 - 2020 96 Table 16: Public Cloud Mgt & Security Services Mkt Rev by Segments 2015 - 2020 96 Table 17: Public Cloud Mgt & Security Services Rev by Segment 2015 - 2020 96 Table 18: Public Cloud BPaaS Services Market Revenue by Segments 2015 - 2020 96 Table 19: Public Cloud BPaaS Services Rev by Segment 2015 - 2020 97 Table 20 Cloud Service Market Revenue by Region 2015 - 2020 97 Table 21: Cloud Service Market Revenue Share by Region 2015 - 2020 97 Table 22: Cloud Application Service Revenue by Industry Vertical 2015 - 2020 98 Table 23: Cloud Application Service Revenue by Industry Verticals 2015 - 2020 98





Table 24: Cloud Application Adoption Trend by Deployment Models 2015 - 2020 98 Table 25: Cloud Application Adoption Trend by Industry Verticals 2015 - 2020 99 Table 26: Cloud Investment in Industry Applications 2015 99 Table 27: Benefits of Cloud App Service Adoption over In-House IT Services 100 Table 28: Basic Features or Functionality of Mobile Personal Cloud Services 154 Global Cloud Computing: Infrastructure, Platforms, and Services 2015 - 2020 1 Introduction 11 1.1 Executive Summary 11 1.2 Topics Covered 12 1.3 Key Findings 13 1.4 Target Audience 14 1.5 Companies Mentioned 15 2 Overview 17 2.1 Enterprise IT Systems Development 17 2.2 Definition of Cloud Computing 17 2.3 "Pay as you Go" Model 20 2.4 Cloud Computing versus Virtualization 21 2.5 Cloud Foundations 21 2.6 Levels of Cloud Computing Services 23 2.6.1 Web Applications 25 2.6.2 Software as a Service (SAAS) 26 2.6.3 Platform as a Service (PAAS) 26 2.6.4 Infrastructure as a Service (IAAS) 27 2.6.4.1 Public cloud 28 2.6.4.2 Private cloud 28 2.6.4.3 Hybrid cloud 28 2.6.5 Communication as a Service (CAAS) 29 2.6.6 Metal as a Service (MAAS) 29 2.6.7 Anything as a Service (XAAS) 30 3 Hardware and Software 31 3.1 Increasing Importance of Hardware 31 3.2 Reduced Need for Provisioning Storage: A Server and a Switch 31 3.3 Cloud Hardware will become a Commodity 31 3.4 Low-power Processors and Cheaper Clouds 32 3.5 Faster Interconnects 32 3.6 Increasing Separation of Software and Hardware 33 3.7 Rise of PAAS 33 3.8 Increasing Popularity of JavaScript/HTML5 based Apps 33 3.9 Stronger Identity Management systems 34 3.10 Memory Solutions for Software and Hardware Systems 34 3.11 Increasing use of Modular Software 34 3.12 Top IAAS Vendors 35





3.13 Top Software Vendors 36 3.14 Data Center Providers 37 3.14.1 Cloud Based Data Centers 37 3.14.1.1 Google, Council Bluffs, Iowa 37 3.14.1.2 Microsoft, Dublin, Republic of Ireland 38 3.14.1.3 Switch Super NAP, Las Vegas, Nevada 39 3.14.1.4 Range International Information Hub, Langfang China 40 3.15 Cloud Service Providers 40 3.15.1 Amazon 40 3.15.2 Verizon 41 3.15.3 IBM 42 3.15.4 SalesForce.com 42 3.15.5 CSC 44 3.15.6 CENTURYLINK 44 3.15.7 SAVVIS 44 3.15.8 JOYENT 44 3.15.9 MICROSOFT 45 3.15.10 RACKSPACE 45 3.15.11 FUJITSU 45 3.15.12 HP 45 3.16 Network Operators 46 3.16.1 China Mobile Limited 46 3.16.2 Vodafone Group 46 3.16.3 Telenor Group 48 3.16.4 America Movil 48 3.17 Alternative Platforms for On-Premise and Hybrid Clouds 48 3.18 Cloud Infrastructure and Services in Telecommunications 58 3.18.1 Cloud RAN 59 3.18.2 Mobile Consumer Cloud Services 62 3.18.2.1 Consumer Mobility and the Cloud: Statistics and Forecasts 66 3.18.2.2 Commercial Considerations 67 3.18.2.2.1 What Consumers will Store in and Access from the Cloud 67 3.18.2.2.2 What Devices Consumers will use to Access the Cloud 68 3.18.2.2.3 Where and How Consumers will Access the Cloud 69 3.18.2.2.4 What Companies do Consumers Identify with Cloud Services 69 4 Future Cloud Computing Applications 71 4.1 Leveraging Cloud for Wearable Technology 71 4.1.1 Wearable Technology is Poised for Significant Growth 71 4.1.2 Cloud-based Services Provide a Platform for Growth 71 4.1.3 Examples of Cloud-services Leveraged by Wearable technology 72 4.1.4 Bottlenecks to Growth of Wearable Technology 73 4.2 Increasing Acceptance in the Government Sector 73 4.2.1 US Government's Consolidation of Datacenters to Cut Costs 74 4.2.1.1 DOI's Movement to a Cloud Computing Environment 74





4.2.1.2 The US Army's Datacenter Consolidation 75 4.2.1.3 CIA's Private Cloud 75 4.2.2 CSPs Building Capacity to Service Government Agencies 75 4.2.3 Leverage Cloud for Citizen Services 76 4.2.4 Leverage Cloud to Support Local Enterprises 76 4.3 Cloud Computing for Media and Entertainment 77 4.3.1 The Case of Netflix 78 4.3.2 A Tool to Increase Subscription Revenues 78 4.3.3 Cloud Services to Develop Stronger Links with Fans 78 4.3.4 Cloud Services to Increase Revenue Generation Capacity 79 4.3.5 Cloud Services to Reduce Datacenter Footprint 79 4.3.6 Cloud Services for Controlling Digital Supply Chain 80 4.4 Cloud Computing for Healthcare 80 4.4.1 US Healthcare Regulations Mandate Cloud Adoption 80 4.4.2 Heavy penalties for loss of PHI 81 4.4.3 Healthcare's Wait and Watch Approach 81 4.4.4 Cloud-based Solutions for Healthcare 81 4.4.5 Adoption of Desktop Virtualization in Healthcare 82 4.4.6 Cloud for Regional Wellness Services 83 4.4.7 Cloud for Penetrating Rural Markets 83 4.5 Cloud Computing for Telecoms 84 4.5.1 The Cloud Expands the Telecoms Service Provider Product Portfolio 84 4.5.2 SME are an Attractive Pocket for Telecom 84 4.5.2.1 SME app market so far ignored by telecoms 84 4.5.2.2 Bundled apps for SMEs 84 4.5.3 Comcast Develops a Cloud Marketplace for SMEs 85 4.5.4 Cloud-based Home Security and Home Monitoring systems 85 4.5.5 More Cloud Opportunities 86 4.5.5.1 AT&T PAAS 86 4.5.5.2 AT&T teamed with IBM on cloud services 86 4.6 Cloud Computing for Insurance 87 4.6.1 Cloud-based Technology Provide Platforms to Increase Market Share 87 4.6.2 Cloud-based Technology Expands Product Portfolio 87 4.7 Cloud Computing for Utilities and Overall Energy Sector 89 4.7.1 Smart Metering in Europe 89 4.7.2 Cloud-based Smart Metering Projects 89 4.7.3 Cloud and Data 90 4.7.4 Cloud and Collaboration 90 4.7.5 Cloud and Production Operations 90 4.7.6 Cloud and Customer Engagement 91 4.8 Cloud Computing for Pharmaceuticals 91 4.8.1 Cloud-based Services are under-utilized by Pharma Companies 91 4.8.2 Leverage Cloud Computing to Reduce Costs and Save Time 92 4.8.3 Cloud-based Digital Market Platform 92





4.8.4 Cloud-based On-demand Analytical Platforms 93 4.8.4.1 GenomicsCloud 93 4.8.4.2 DNAnexus 93 4.8.4.3 CycleCloud for Life Sciences 94 4.8.5 Shifting Non-core Operations to the Cloud 94 4.9 Cloud Computing for Financial Services 94 4.9.1 Small Banks find it Easier to Shift to the Cloud 95 4.9.2 Cloud-based Payments Processing 95 4.9.3 Cloud-based Document Management 96 4.9.4 Bank of America's Pilot Test with the Cloud 96 4.9.5 Cloud-based Mobile Banking 97 4.9.6 Regulatory Barriers Restrict Wide Adoption of Cloud-based Analytics 97 4.9.7 Cloud-based Monte Carlo Simulations 98 4.9.8 Private Clouds 98 5 Recommendations for Enterprise 99 5.1 When Cloud Computing is Not an Option 99 5.1.1 Risk of External Dependencies 99 5.1.1.1 Data protection tools 99 5.1.2 Scalability Comes at a Cost 99 5.2 Approach Cloud Computing from a Strategic Perspective 100 5.2.1 What Enterprise Must Consider when Developing a Cloud Strategy 101 5.3 Trade-off between Location of Datacenter and Service Portfolio 102 5.4 Enterprises Must Have Strict Security and Compliance Levels 102 5.5 Develop a Realistic Cost Estimate 103 5.5.1 Allocate for Hidden costs 103 5.6 Conduct CSP Background Checks 104 5.7 Add Substance to the Term "Security" 104 5.7.1 Data Security Measures 104 5.7.2 Physical and Personnel Protections and Restrictions 105 5.7.3 Geographic Location of Datacenter 105 5.8 Understand Audit Options 106 5.8.1 Existing Service Standards are Non-comprehensive 106 5.8.2 Add "Right to Audit" Clause in the Contract 106 5.9 Viability of Third-party Providers 107 5.10 Formulate an Exit Strategy 107 5.10.1 Prepare for Enterprise's Exit 107 5.10.2 Prepare for CSP's Exit 108 5.11 Evaluate all Applications in a Step-by-Step Approach 109 5.12 Review the Contract in Detail 109 5.12.1 Prepare a Service Checklist 109 5.12.1.1 Plan for protecting your data: 110 5.12.1.2 Incident response: 110 5.12.1.3 Data lifecycle management: 110 5.12.1.4 Vulnerability management: 111





6 Recommendations for Cloud Service Providers 112 6.1 Work Side-by-Side with the Customer 112 6.2 Understand your Customer 112 6.3 Develop Marketing Plan for a Non-technical Audience 113 6.4 Stress Heavily on Security Measures 113 6.4.1 Customer's Perception of Security is Central to Aligning Needs with Capabilities 113 6.4.2 Match SMEs Security Requirements 114 6.4.3 Security Requirements vary from Customer-to-Customer 114 6.4.4 SMEs Resist Installing Firewalls to Minimize Costs 114 6.4.5 Demonstrate Security Measures 115 6.5 Determine the Optimal Price 115 6.5.1 Adjust Pricing strategy as required 115 6.5.2 Understand Customer's Existing IT Costs 116 6.6 Reorient Sales Team 116 6.7 Provide Support with Integration 117 6.8 Guidelines for Making a Sales Pitch 117 6.9 Educating SMEs 118 7 Global Cloud Computing Market Forecasts 2015 - 2020 121 7.1 Global Cloud Computing Market Value 2015 - 2020 121 7.2 Market Value by Segment 2015 - 2020 122 7.2.1 Segment Market Share 2015 - 2020 123 7.2.2 Segment Growth Rate 2015 - 2020 124 7.2.3 SAAS Market Value and Growth Rate 2015 - 2020 125 7.2.4 IAAS Market Value and Growth Rate 2015 - 2020 126 7.2.5 PAAS Market Value and Growth Rate 2015 - 2020 127 7.3 Market Value by Region 2015 - 2020 128 7.3.1 Region Market Share 2015 - 2020 129 7.3.2 Region Growth Rate 2015 - 2020 130 7.3.3 North America Market Value and Growth Rate 2015 - 2020 131 7.3.4 Western Europe Market Value and Growth Rate 2015 - 2020 132 7.3.5 Eastern Europe Market Value and Growth Rate 2015 - 2020 133 7.3.6 Middle East and Africa Market Value and Growth Rate 2015 - 2020 134 7.3.7 Asia Pacific Market Value and Growth Rate 2015 - 2020 135 7.3.8 Rest-of-World Market Value and Growth Rate 2015 - 2020 136 7.4 Market Value by Cloud Type (Public and Private) 2015 - 2020 137 7.4.1 Market Share by Cloud Type 2015 - 2020 138 7.4.2 Growth Rates by Cloud Type 2015 - 2020 139 7.4.3 Public Cloud Market Value and Growth Rate 2015 - 2020 140 7.4.4 Private Cloud Market Value and Growth Rate 2015 - 2020 141 8 Barriers and Challenges to Cloud Adoption 142 8.1 Enterprises Reluctance to Change 142 8.2 Responsibility of Data Security Externalized 142 8.2.1 Loss of Control 142 8.2.2 Privacy 143





8.3 Security Concerns are Real 144 8.4 Cyberattacks 144 8.4.1 SMEs Operate with Severe Budget Restrictions 144 8.4.2 Prolific use of Internet Increases Threats of Cyber-attacks 145 8.5 Unclear Agreements 145 8.5.1 SLAs do not Guarantee Downtime Requirements will be Met 145 8.5.2 Secondary CSPs negotiate contracts on a contract to contract basis 146 8.5.3 Enterprises are Entitled to Credit for downtime "On-Request" 146 8.5.4 Varying Timeframes for Calculating Uptime 146 8.5.5 Different Cloud Services have Different SLAs 146 8.6 Complexity is a Deterrent 147 8.6.1 Inherent Complexity in the Cloud computing Environment 147 8.6.2 Integrating Enterprise Processes around the Cloud is a Complex Task 147 8.6.3 Integration is a Big Issue with SAAS Deployment 147 8.6.3.1 API management is a necessary task 148 8.6.3.2 What is the best way to integrate data 148 8.7 Lack of Cloud Interoperability 148 8.7.1 Denial of Switching CSPs and Clouds 148 8.7.2 CSPs Opt-out of Cloud Interoperability 149 8.7.3 Challenges Faced when Moving Applications between Clouds 149 8.8 Service Provider Resistance to Audits 149 8.8.1 Industry Best Practices are Still Developing 149 8.8.2 Resistance to Audit Signals Caution 150 8.8.2.1 Internal quality controls of large enterprises make audits essential 150 8.8.2.2 Audits inspire confidence among reluctant SMEs 150 8.9 Viability of Third-party Providers 150 8.10 Acceptance Issues 151 8.11 No Move of Systems and Data is without Cost 151 8.12 Lack of Integration Features in the Public Cloud results in Reduced Functionality 152 Figures Figure 1: Cloud Computing 19 Figure 2: Cloud Computing Services 25 Figure 3: Datacenter 38 Figure 4: Top Cloud Service Providers 43 Figure 5: Virtualization of the Mobile Network 47 Figure 6: Cloud Radio Access Network (C-RAN) 60 Figure 7: How People User their Mobile Phone 64 Figure 8: Personal Content on Home Computer and Mobile Device 65 Figure 9: Wearable Technology Devices 71 Figure 10: Global Cloud Computing Market Value and Growth Rate 2015 - 2020 121 Figure 11: Market Value by segment 2015 - 2020 122 Figure 12: Market Share by Segment 2015 - 2020 123





Figure 13: Segment Growth Rate 2015 - 2020 124 Figure 14: SAAS Market Value and Growth Rate 2015 - 2020 125 Figure 15: IAAS market Value and Growth Rate 2015 - 2020 126 Figure 16: PAAS Market Value and Growth Rate 2015 - 2020 127 Figure 17: Market Value by Region 2015 - 2020 128 Figure 18: Market Share by Region 2015 - 2020 129 Figure 19: Region Growth Rate 2015 - 2020 130 Figure 20: North America Market Value and Growth Rate 2015 - 2020 131 Figure 21: Western Europe Market Value and Growth Rate 2015 - 2020 132 Figure 22: Eastern Europe Market Value and Growth Rate 2015 - 2020 133 Figure 23: Middle East and Africa Market Value and Growth Rate 2015 - 2020 134 Figure 24: Asia Pacific Market Value and Growth Rate 2015 - 2020 135 Figure 25: Rest of the World Value and Growth Rate 2015 - 2020 136 Figure 26: Market Value by Cloud Type (Public vs. Private) 2015 - 2020 137 Figure 27: Market Share by Cloud Type (Public vs. Private) 2015 - 2020 138 Figure 28: Growth Rates by Cloud Type (Public vs. Private) 2015 - 2020 139 Figure 29: Public Cloud Market Value and Growth Rate 2015 - 2020 140 Figure 30: Private Cloud Market Value and Growth Rate 2015 - 2020 141 Data as a Service (DaaS) Market and Forecasts 2015 - 2020 1 Introduction 8 1.1 Executive Summary 8 1.2 Topics Covered 10 1.3 Key Findings 11 1.4 Target Audience 12 2 DaaS Technologies 13 2.1 Cloud 13 2.2 Database Approaches and Solutions 14 2.2.1 Relational Database Management System (RDBS) 14 2.2.2 NoSQL 15 2.2.3 Hadoop 16 2.2.4 High Performance Computing Cluster (HPCC) 18 2.2.5 OpenStack 19 2.3 DaaS and the XaaS Ecosystem 19 2.4 Open Data Center Alliance 22 2.5 Market Sizing by Horizontal 23 3 DaaS Market 25 3.1 Market Overview 25 3.1.1 Data-as-a-Service: A movement 27 3.1.2 Data Structure 27 3.1.3 Specialization 28 3.1.4 Vendors 30 3.2 Vendor Analysis and Prospects 31





3.2.1 Large Vendors: BDaaS 31 3.2.2 Mid-sized Vendors 35 3.2.3 Small Vendors: DaaS and SaaS 37 3.2.4 Market Size: BDaaS vs. RDBMS 38 3.3 Market Drivers and Constraints 39 3.3.1 Drivers 39 3.3.1.1 Business Intelligence and DaaS Integration 42 3.3.1.2 The Cloud Enabler DaaS 44 3.3.1.3 XaaS Drives DaaS 44 3.3.2 Constraints 44 3.3.2.1 Issues Relating to Data-as-a-Service Integration 47 3.4 Barriers and Challenges to DaaS Adoption 48 3.4.1 Enterprises Reluctance to Change 48 3.4.2 Responsibility of Data Security Externalized 49 3.4.3 Security Concerns are Real 49 3.4.4 Cyber Attacks 50 3.4.5 Unclear Agreements 51 3.4.6 Complexity is a Deterrent 53 3.4.7 Lack of Cloud Interoperability 54 3.4.8 Service Provider Resistance to Audits 55 3.4.9 Viability of Third-party Providers 56 3.4.10 No Move of Systems and Data is without Cost 57 3.4.11 Lack of Integration Features in the Public Cloud results in Reduced Functionality 58 3.5 Market Share and Geographic Influence 58 3.6 Vendors 61 3.6.1 1010data 62 3.6.2 Amazon 62 3.6.3 Clickfox 65 3.6.4 Datameer 66 3.6.5 Google 66 3.6.6 Hewlett-Packard 68 3.6.7 IBM 69 3.6.8 Infosys 70 3.6.9 Microsoft 71 3.6.10 Oracle 71 3.6.11 Rackspace 72 3.6.12 Salesforce 73 3.6.13 Splunk 74 3.6.14 Teradata 74 3.6.15 Tresata 76 4 DaaS Strategies 77 4.1 General Strategies 77 4.1.1 Tiered Data Focus 77 4.1.2 Value-based Pricing 79





4.1.3 Open Development Environment 80 4.2 Specific Strategies 81 4.2.1 Service Ecosystem and Platforms 81 4.2.2 Bringing to Together Multiple Sources for Mash-ups 82 4.2.3 Developing Value-added Services (VAS) as Proof Points 83 4.2.4 Open Access to all Entities including Competitors 83 4.2.5 Prepare for Big Opportunities with the Internet of Things (IoT) 84 4.3 Service Provider Strategies 88 4.3.1 Telecom Network Operators 88 4.3.2 Data Center Providers 96 4.3.3 Managed Service Providers 97 4.4 Infrastructure Provider Strategies 98 4.4.1 Enable New Business Models 98 4.5 Application Developer Strategies 99 5 DaaS based Applications 100 5.1 Business Intelligence 100 5.2 Development Environments 103 5.3 Verification and Authorization 104 5.4 Reporting and Analytics 105 5.5 DaaS in Healthcare 106 5.6 DaaS and Wearable technology 107 5.7 DaaS in the Government Sector 107 5.8 DaaS for Media and Entertainment 108 5.9 DaaS for Telecoms 109 5.10 DaaS for Insurance 110 5.11 DaaS for Utilities and Energy Sector 110 5.12 DaaS for Pharmaceuticals 111 5.13 DaaS for Financial Services 111 6 Market Outlook and Future of DaaS 113 6.1 Recent Security Concerns 113 6.2 Cloud Trends 116 6.2.1 Hybrid Computing 117 6.2.2 Multi-Cloud 118 6.2.3 Cloud Bursting 119 6.3 General Data Trends 121 6.4 Enterprise Leverages own Data and Telecom 123 6.4.1 Web APIs 123 6.4.2 SOA and Enterprise APIs 125 6.4.3 Cloud APIs 127 6.4.4 Telecom APIs 128 6.5 Data Federation Emerges for DaaS 130 7 Conclusions 138 8 Appendix 141 8.1 Structured vs. Unstructured Data 141





8.1.1 Structured Database Services in Telecom 141 8.1.2 Unstructured Database Services in Telecom and Enterprise 143 8.1.3 Emerging Hybrid (Structured/Unstructured) Database Services 143 8.2 Data Architecture and Functionality 146 8.2.1 Data Architecture 146 8.2.1.1 Data Models and Modelling 147 8.2.1.2 DaaS Architecture 148 8.2.2 Data Mart vs. Data Warehouse 150 8.2.3 Data Gateway 151 8.2.4 Data Mediation 151 8.3 Master Data Management (MDM) 155 8.3.1 Understanding MDM 156 8.3.1.1 Transactional vs. Non-transactional Data 157 8.3.1.2 Reference vs. Analytics Data 157 8.3.2 MDM and DaaS 157 8.3.2.1 Data Acquisition and Provisioning 158 8.3.2.2 Data Warehousing and Business Intelligence 159 8.3.2.3 Analytics and Virtualization 160 8.3.2.4 Data Governance 160 8.4 Data Mining 161 8.4.1 Data Capture 163 8.4.1.1 Event Detection 165 8.4.1.2 Capture Methods 165 8.4.2 Data Mining Tools 168 Figures Figure 2: Cloud Computing Service Model Stack and Principle Consumers 20 Figure 3: DaaS across Horizontal and Vertical Segments 22 Figure 8: Different Data Types and Functions in DaaS 78 Figure 9: Ecosystem and Platform Model 81 Figure 10: Ecosystem and Platform Model 85 Figure 11: DaaS and IoT Mediation for Smartgrid 87 Figure 12: Internet of Things (IoT) and DaaS 88 Figure 13: Telecom API Value Chain for DaaS 95 Figure 14: DaaS, Verification and Authorization 104 Figure 15: Web APIs 124 Figure 16: Services Oriented Architecture 126 Figure 17: Cloud Services, DaaS, and APIs 128 Figure 18: Telecom APIs 129 Figure 19: Federated Data vs. Non-Federated Models 131 Figure 20: Federated Data at Functional Level 133 Figure 21: Federated Data at City Level 134 Figure 22: Federated Data at Global Level 135





Figure 23: Federation Requires Mediation Data 136 Figure 24: Mediation Data Synchronization 137 Figure 25: Hybrid Data in Next Generation Applications 145 Figure 26: Traditional Data Architecture 146 Figure 27: Data Architecture Modeling 147 Figure 28: DaaS Data Architecture 149 Figure 29: Location Data Mediation 152 Figure 30: Data Mediation in IoT 153 Figure 31: Data Mediation for Smartgrids 155 Figure 32: Enterprise Data Types 156 Figure 33: Data Governance 161 Figure 34: Data Flow 163 Figure 35: Processing Streaming Data 164

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