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  3. > Communication Service Provider B2B Data Services: Telecom APIs and Data as a Service (DaaS) 2015 - 2020

Overview:

Global Communication Service Providers (CSP) have reached saturation with respect to customer penetration. Furthermore, core voice and messaging services are becoming increasingly marginalized. As fourth generation (4G) cellular via LTE is optimized globally via LTE Advanced (LTE-A), raw data services are next to realize ever shrinking margins. Recognizing this issue, certain leading CSPs offer telecom data Business-to-Business (B2B) services in a Data as a Service (DaaS) basis to various third party companies such as Over-the-Top (OTT) application providers.

Telecom data is provided over Application Programming Interfaces (API) from various CSP databases including number portability, messaging, location, and subscriber databases to name a few. Enterprise customers, OTT players, and others pay CSPs for data in a DaaS business model. This model is expected to expand globally beyond the current large CSPs to smaller CSPs, many of which are located in developing countries.

Additionally, Mind Commerce anticipates the coming of a Telecom API enabled Application Marketplace. This marketplace will be in many ways similar to those of Google and Apple with the key difference that apps rely upon CSP data delivered via APIs in a DaaS B2B business model. Finally, we see CSPs ultimately realizing that they must embrace the app marketplace to offer their own Value-added Service (VAS) apps, which they will initially target for their most important customers: enterprise.

This research evaluates CSP B2B data services opportunities. The report provides an in-depth assessment of the global Telecom Network API market, including business models, business case, best practices, value chain analysis, operator and vendor strategies, vision for the future of telecom data, and forecasts for 2015 to 2020. The report also evaluates the DaaS ecosystem including technologies, companies, and solutions. The report assesses market opportunities and provides a market outlook and forecast for 2015 to 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:
App Developers
API Aggregators
Mobile Device Vendors
Mobile Network Carriers
Data services companies
Cloud services companies
Service Bureau Companies
Data infrastructure providers
Wireless Infrastructure Vendors
Network and application integrators
Intermediaries and mediation companies
Major enterprise and businesses of all types

Report Benefits:
Forecast for DaaS through 2020
Understand the DaaS ecosystem
Identify key players and strategies
Understand DaaS technologies and tools
Recognize the importance of data mediation
Understand data management best practices
Understand the importance of managed systems
Identify the relationship between DaaS and cloud
Telecom API marketplace forecasts for 2015 - 2020
Insights from Telecom API use cases and business cases
Understand the Telecom and Enterprise API marketplace
Understand key API technologies and inter-dependencies
Identify advantages of internal Telecom API usage to carriers
Identify the long-term growth drivers for the API marketplace
Understand how the API marketplace relates to the Cloud and DaaS
Understand the untapped potential for carrier Telecom API enabled VAS

Table Of Contents

Communication Service Provider B2B Data Services: Telecom APIs and Data as a Service (DaaS) 2015 - 2020
Table of Contents:

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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