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Carrier B2B Data Revenue: Big Data, Analytics, and Data as a Service (DaaS) 2014 - 2019

Published: December 2014
Pages: 229
Pricing:

Single-user License: $ 2,995 USD
Team License (Up to 5 Users): $ 3,995 USD
Company-wide License: $ 6,995 USD

Keywords: Big Data, Telecom Analytics, DaaS, Data as a Service, SDM, Subscriber Data Management, Carrier B2C, Carrier B2B, B2B2C, B2B2B

Overview

Telecommunications service providers acquire and maintain substantial structured and unstructured (Big) data. Leading carriers have centralized Subscriber Data Management (SDM) systems, which consolidate and organize data from various sources such as HLR, HSS, and other data repositories. In addition, carriers have access to a plethora of data from various "Big Data" sources such as OSS/BSS, system monitoring and performance management systems including Self Organizing Networks (SON).

Big Data and related Analytics solutions opens a vast array of applications and opportunities for telecom carriers to offer services in multiple industry verticals. Network operators may sell data in a "Data as a Service" (DaaS) model to various market sectors including retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical. DaaS is defined as any service offered wherein users can access vendor provided databases or host their own databases on vendor managed systems. DaaS is expected to grow significantly in the near future due to a few dominant themes including cloud-based infrastructure/services, enterprise data syndication, and the consumer services trend towards Everything as a Service (XaaS).

Carriers have an excellent opportunity to offer Business-to-Business (B2B) services on a DaaS basis, representing a fast growing secondary and revenue stream. The Big Data driven telecom analytics market alone is expect to grow at a CAGR of nearly 50% between 2014 and 2019. By the end of 2019, the market will eventually account for $5.4 Billion in annual revenue.

This research provides a quantitative and qualitative and assessment of carrier prospects for B2B revenue as a DaaS provider including forecast data and key insights respectively. All purchases of Mind Commerce reports includes time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Target Audience:
• Telecom service providers
• Wireless device manufacturers
• Big Data and Analytics companies
• Wireless infrastructure companies
• Telecom managed service companies
• Cloud infrastructure and XaaS providers
• Intermediaries and mediation companies
Report Benefits:
• Forecast data for Big Data, Analytics and DaaS to 2019
• Understand DaaS infrastructure challenges for operations
• Identify carrier Big Data solutions and XaaS service packages
• Recognize the role and importance of DaaS as service offering
• Understand the importance of managed systems and best practices
• Understand Big Data vendor landscape, value chain analysis, case studies
Table of Contents:

Big Data and Telecom Analytics Market: Business Case, Market Analysis & Forecasts 2014 - 2019

1 Chapter 1: Introduction 8
1.1 Executive Summary 8
1.2 Topics Covered 9
1.3 Key Findings 10
1.4 Target Audience 11
1.5 Companies Mentioned 12
2 Chapter 2: Big Data Technology & Business Case 15
2.1 Defining Big Data 15
2.2 Key Characteristics of Big Data 15
2.2.1 Volume 15
2.2.2 Variety 16
2.2.3 Velocity 16
2.2.4 Variability 16
2.2.5 Complexity 16
2.3 Big Data Technology 17
2.3.1 Hadoop 17
2.3.1.1 MapReduce 17
2.3.1.2 HDFS 17
2.3.1.3 Other Apache Projects 18
2.3.2 NoSQL 18
2.3.2.1 Hbase 18
2.3.2.2 Cassandra 18
2.3.2.3 Mongo DB 18
2.3.2.4 Riak 19
2.3.2.5 CouchDB 19
2.3.3 MPP Databases 19
2.3.4 Others and Emerging Technologies 20
2.3.4.1 Storm 20
2.3.4.2 Drill 20
2.3.4.3 Dremel 20
2.3.4.4 SAP HANA 20
2.3.4.5 Gremlin & Giraph 20
2.4 Market Drivers 21
2.4.1 Data Volume & Variety 21
2.4.2 Increasing Adoption of Big Data by Enterprises & Telcos 21
2.4.3 Maturation of Big Data Software 21
2.4.4 Continued Investments in Big Data by Web Giants 21
2.5 Market Barriers 22
2.5.1 Privacy & Security: The 'Big' Barrier 22
2.5.2 Workforce Re-skilling & Organizational Resistance 22
2.5.3 Lack of Clear Big Data Strategies 23
2.5.4 Technical Challenges: Scalability & Maintenance 23
3 Chapter 3: Key Investment Sectors for Big Data 24
3.1 Industrial Internet & M2M 24
3.1.1 Big Data in M2M 24
3.1.2 Vertical Opportunities 24
3.2 Retail & Hospitality 25
3.2.1 Improving Accuracy of Forecasts & Stock Management 25
3.2.2 Determining Buying Patterns 25
3.2.3 Hospitality Use Cases 25
3.3 Media 26
3.3.1 Social Media 26
3.3.2 Social Gaming Analytics 26
3.3.3 Usage of Social Media Analytics by Other Verticals 26
3.4 Utilities 27
3.4.1 Analysis of Operational Data 27
3.4.2 Application Areas for the Future 27
3.5 Financial Services 27
3.5.1 Fraud Analysis & Risk Profiling 27
3.5.2 Merchant-Funded Reward Programs 27
3.5.3 Customer Segmentation 28
3.5.4 Insurance Companies 28
3.6 Healthcare & Pharmaceutical 28
3.6.1 Drug Development 28
3.6.2 Medical Data Analytics 28
3.6.3 Case Study: Identifying Heartbeat Patterns 28
3.7 Telcos 29
3.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization 29
3.7.2 Speech Analytics 29
3.7.3 Other Use Cases 29
3.8 Government & Homeland Security 30
3.8.1 Developing New Applications for the Public 30
3.8.2 Tracking Crime 30
3.8.3 Intelligence Gathering 30
3.8.4 Fraud Detection & Revenue Generation 30
3.9 Other Sectors 31
3.9.1 Aviation: Air Traffic Control 31
3.9.2 Transportation & Logistics: Optimizing Fleet Usage 31
3.9.3 Sports: Real-Time Processing of Statistics 31
4 Chapter 4: The Big Data Value Chain 32
4.1 How Fragmented is the Big Data Value Chain? 32
4.2 Data Acquisitioning & Provisioning 33
4.3 Data Warehousing & Business Intelligence 33
4.4 Analytics & Virtualization 33
4.5 Actioning & Business Process Management (BPM) 34
4.6 Data Governance 34
5 Chapter 5: Big Data in Telco Analytics 35
5.1 How Big is the Market for Telco Analytics? 35
5.2 Improving Subscriber Experience 36
5.2.1 Generating a Full Spectrum View of the Subscriber 36
5.2.2 Creating Customized Experiences and Targeted Promotions 36
5.2.3 Central 'Big Data' Repository: Key to Customer Satisfaction 36
5.2.4 Reduce Costs and Increase Market Share 37
5.3 Building Smarter Networks 37
5.3.1 Understanding the Usage of the Network 37
5.3.2 The Magic of Analytics: Improving Network Quality and Coverage 37
5.3.3 Combining Telco Data with Public Data Sets: Real-Time Event Management 37
5.3.4 Leveraging M2M for Telco Analytics 37
5.3.5 M2M, Deep Packet Inspection & Big Data: Identifying & Fixing Network Defects 38
5.4 Churn/Risk Reduction and New Revenue Streams 38
5.4.1 Predictive Analytics 38
5.4.2 Identifying Fraud & Bandwidth Theft 38
5.4.3 Creating New Revenue Streams 39
5.5 Telco Analytics Case Studies 39
5.5.1 T-Mobile USA: Churn Reduction by 50% 39
5.5.2 Vodafone: Using Telco Analytics to Enable Navigation 39
6 Chapter 6: Key Players in the Big Data Market 41
6.1 Vendor Assessment Matrix 41
6.2 Apache Software Foundation 42
6.3 Accenture 42
6.4 Amazon 42
6.5 APTEAN (Formerly CDC Software) 43
6.6 Cisco Systems 43
6.7 Cloudera 43
6.8 Dell 43
6.9 EMC 44
6.10 Facebook 44
6.11 GoodData Corporation 44
6.12 Google 44
6.13 Guavus 45
6.14 Hitachi Data Systems 45
6.15 Hortonworks 45
6.16 HP 46
6.17 IBM 46
6.18 Informatica 46
6.19 Intel 46
6.20 Jaspersoft 47
6.21 Microsoft 47
6.22 MongoDB (Formerly 10Gen) 47
6.23 MU Sigma 48
6.24 Netapp 48
6.25 Opera Solutions 48
6.26 Oracle 48
6.27 Pentaho 49
6.28 Platfora 49
6.29 Qliktech 49
6.30 Quantum 50
6.31 Rackspace 50
6.32 Revolution Analytics 50
6.33 Salesforce 51
6.34 SAP 51
6.35 SAS Institute 51
6.36 Sisense 51
6.37 Software AG/Terracotta 52
6.38 Splunk 52
6.39 Sqrrl 52
6.40 Supermicro 53
6.41 Tableau Software 53
6.42 Teradata 53
6.43 Think Big Analytics 54
6.44 Tidemark Systems 54
6.45 VMware (Part of EMC) 54
7 Chapter 7: Market Analysis 55
7.1 Big Data Revenue: 2014 - 2019 55
7.2 Big Data Revenue by Functional Area: 2014 - 2019 56
7.2.1 Supply Chain Management 57
7.2.2 Business Intelligence 58
7.2.3 Application Infrastructure & Middleware 59
7.2.4 Data Integration Tools & Data Quality Tools 60
7.2.5 Database Management Systems 61
7.2.6 Big Data Social & Content Analytics 62
7.2.7 Big Data Storage Management 63
7.2.8 Big Data Professional Services 64
7.3 Big Data Revenue by Region 2014 - 2019 65
7.3.1 Asia Pacific 66
7.3.2 Eastern Europe 67
7.3.3 Latin & Central America 68
7.3.4 Middle East & Africa 69
7.3.5 North America 70
7.3.6 Western Europe 71

Figures

Figure 1: The Big Data Value Chain 32
Figure 2: Telco Analytics Investments Driven by Big Data: 2013 - 2019 ($ Million) 35
Figure 3: Big Data Vendor Ranking Matrix 2013 41
Figure 4: Big Data Revenue: 2013 - 2019 ($ Million) 55
Figure 5: Big Data Revenue by Functional Area: 2013 - 2019 ($ Million) 56
Figure 6: Big Data Supply Chain Management Revenue: 2013 - 2019 ($ Million) 57
Figure 7: Big Data Supply Business Intelligence Revenue: 2013 - 2019 ($ Million) 58
Figure 8: Big Data Application Infrastructure & Middleware Revenue: 2013 - 2019 ($ Million) 59
Figure 9: Big Data Integration Tools & Data Quality Tools Revenue: 2013 - 2019 ($ Million) 60
Figure 10: Big Data Database Management Systems Revenue: 2013 - 2019 ($ Million) 61
Figure 11: Big Data Social & Content Analytics Revenue: 2013 - 2019 ($ Million) 62
Figure 12: Big Data Storage Management Revenue: 2013 - 2019 ($ Million) 63
Figure 13: Big Data Professional Services Revenue: 2013 - 2019 ($ Million) 64
Figure 14: Big Data Revenue by Region: 2013 - 2019 ($ Million) 65
Figure 15: Asia Pacific Big Data Revenue: 2013 - 2019 ($ Million) 66
Figure 16: Eastern Europe Big Data Revenue: 2013 - 2019 ($ Million) 67
Figure 17: Latin & Central America Big Data Revenue: 2013 - 2019 ($ Million) 68
Figure 18: Middle East & Africa Big Data Revenue: 2013 - 2019 ($ Million) 69
Figure 19: North America Big Data Revenue: 2013 - 2019 ($ Million) 70
Figure 20: Western Europe Big Data Revenue: 2013 - 2019 ($ Million) 71

Data as a Service (DaaS) Market and Forecasts 2014 - 2019

1 Introduction 8
1.1 Executive Summary 8
1.2 Topics Covered 10
1.3 Key Findings 11
2 DaaS Technologies 12
2.1 Cloud 12
2.2 Database Approaches and Solutions 13
2.2.1 Relational Database Management System (RDBS) 13
2.2.2 NoSQL 14
2.2.3 Hadoop 15
2.2.4 High Performance Computing Cluster (HPCC) 17
2.2.5 OpenStack 18
2.3 DaaS and the XaaS Ecosystem 18
2.4 Open Data Center Alliance 21
2.5 Market Sizing by Horizontal 22
3 DaaS Market 23
3.1 Market Overview 23
3.1.1 Data Structure 24
3.1.2 Specialization 25
3.1.3 Vendors 27
3.2 Vendor Analysis and Prospects 28
3.2.1 Large Vendors: BDaaS 28
3.2.2 Mid-sized Vendors 33
3.2.3 Small Vendors: DaaS and SaaS 34
3.2.4 Market Size: BDaaS vs. RDBMS 36
3.3 Market Drivers and Constraints 37
3.3.1 Drivers 37
3.3.2 Constraints 39
3.4 Market Share and Geographic Influence 41
3.5 Vendors 45
3.5.1 1010data 45
3.5.2 Amazon 45
3.5.3 Clickfox 49
3.5.4 Datameer 49
3.5.5 Google 49
3.5.6 Hewlett-Packard 51
3.5.7 IBM 52
3.5.8 Infosys 54
3.5.9 Microsoft 54
3.5.10 Oracle 55
3.5.11 Rackspace 56
3.5.12 Salesforce 57
3.5.13 Splunk 57
3.5.14 Teradata 58
3.5.15 Tresata 59
4 DaaS Strategies 60
4.1 General Strategies 60
4.1.1 Tiered Data Focus 60
4.1.2 Value-based Pricing 62
4.1.3 Open Development Environment 63
4.2 Specific Strategies 64
4.2.1 Service Ecosystem and Platforms 64
4.2.2 Bringing to Together Multiple Sources for Mash-ups 66
4.2.3 Developing Value-added Services (VAS) as Proof Points 66
4.2.4 Open Access to all Entities including Competitors 66
4.2.5 Prepare for Big Opportunities with the Internet of Things (IoT) 67
4.3 Service Provider Strategies 71
4.3.1 Telecom Network Operators 71
4.3.2 Data Center Providers 79
4.3.3 Managed Service Providers 80
4.4 Infrastructure Provider Strategies 81
4.4.1 Enable New Business Models 81
4.5 Application Developer Strategies 82
5 DaaS based Applications 83
5.1 Business Intelligence 83
5.2 Development Environments 86
5.3 Verification and Authorization 87
5.4 Reporting and Analytics 88
5.5 Development Environments 89
6 Market Outlook and Future of DaaS 90
6.1 Recent Security Concerns 90
6.2 Cloud Trends 93
6.2.1 Hybrid Computing 94
6.2.2 Multi-Cloud 95
6.2.3 Cloud Bursting 96
6.3 General Data Trends 98
6.4 Enterprise Leverages own Data and Telecom 100
6.4.1 Web APIs 100
6.4.2 SOA and Enterprise APIs 102
6.4.3 Cloud APIs 104
6.4.4 Telecom APIs 105
6.5 Data Federation Emerges for DaaS 107
7 Conclusions 115
8 Appendix 118
8.1 Structured vs. Unstructured Data 118
8.1.1 Structured Database Services in Telecom 118
8.1.2 Unstructured Database Services in Telecom and Enterprise 120
8.1.3 Emerging Hybrid (Structured/Unstructured) Database Services 120
8.2 Data Architecture and Functionality 123
8.2.1 Data Architecture 123
8.2.1.1 Data Models and Modelling 124
8.2.1.2 DaaS Architecture 125
8.2.2 Data Mart vs. Data Warehouse 127
8.2.3 Data Gateway 128
8.2.4 Data Mediation 128
8.3 Master Data Management (MDM) 132
8.3.1 Understanding MDM 133
8.3.1.1 Transactional vs. Non-transactional Data 134
8.3.1.2 Reference vs. Analytics Data 134
8.3.2 MDM and DaaS 134
8.3.2.1 Data Acquisition and Provisioning 135
8.3.2.2 Data Warehousing and Business Intelligence 136
8.3.2.3 Analytics and Virtualization 137
8.3.2.4 Data Governance 137
8.4 Data Mining 138
8.4.1 Data Capture 140
8.4.1.1 Event Detection 142
8.4.1.2 Capture Methods 142
8.4.2 Data Mining Tools 145

Figures

Figure 1: Total DaaS Revenue, Through 2019 8
Figure 2: Cloud Computing Service Model Stack and Principle Consumers 19
Figure 3: DaaS across Horizontal and Vertical Segments 21
Figure 4: Revenue by XaaS Horizontal 2014 - 2019 22
Figure 5: BDaaS Revenue by Vertical 2014 - 2019 32
Figure 6: BDaaS Revenue by Vertical 2014 - 2019 36
Figure 7: DaaS Revenue by Region 2014 - 2019 44
Figure 8: Different Data Types and Functions in DaaS 61
Figure 9: Ecosystem and Platform Model 64
Figure 10: Ecosystem and Platform Model 68
Figure 11: DaaS and IoT Mediation for Smartgrid 70
Figure 12: Internet of Things (IoT) and DaaS 71
Figure 13: Telecom API Value Chain for DaaS 78
Figure 14: DaaS, Verification and Authorization 87
Figure 15: Web APIs 101
Figure 16: Services Oriented Architecture 103
Figure 17: Cloud Services, DaaS, and APIs 105
Figure 18: Telecom APIs 106
Figure 19: Federated Data vs. Non-Federated Models 108
Figure 20: Federated Data at Functional Level 110
Figure 21: Federated Data at City Level 111
Figure 22: Federated Data at Global Level 112
Figure 23: Federation Requires Mediation Data 113
Figure 24: Mediation Data Synchronization 114
Figure 25: Hybrid Data in Next Generation Applications 122
Figure 26: Traditional Data Architecture 123
Figure 27: Data Architecture Modeling 124
Figure 28: DaaS Data Architecture 126
Figure 29: Location Data Mediation 129
Figure 30: Data Mediation in IoT 130
Figure 31: Data Mediation for Smartgrids 132
Figure 32: Enterprise Data Types 133
Figure 33: Data Governance 138
Figure 34: Data Flow 140
Figure 35: Processing Streaming Data 141

Everything as a Service (XaaS)

1 Introduction 5
1.1 Executive Summary 5
1.2 XaaS: Market Driver for DaaS 6
2 The SPI Model (SaaS, Paas and Iaas) 7
2.1 Software as a Service (SaaS) 7
2.2 Infrastructure as a Service (IaaS) 11
2.3 Platform as a Service (PaaS) 12
3 Benefits for the Enterprise 14
3.1 Market Forecasts 2014 - 2018 15
3.2 Transforming Enterprise Operations into the Cloud: Benefits and Challenges 17
4 Everything as a Service (Xaas) 20
4.1 Storage as a Service (STORaaS) 20
4.2 Communication as a Service (CaaS) 21
4.3 Network as a Service (NaaS) 22
4.4 Monitoring as a Service (MaaS) 22
4.5 Back-up as a Service (BaaS) 23
4.6 Desktop as a Service (DTaaS) 24
4.7 Database as a Service (DBaaS) 26
4.8 Big Data as a Service (BDaaS) 28
4.9 Identity as a Service (IDaaS) 30
4.10 Management as a Service (MGTaaS) 31
4.11 Business Process as a Service (BPaaS) 31
4.12 Proximity as a Service (PROXaaS) 32
4.13 XaaS Future Direction 35
5 XaaS Vendors Landscape 36
5.1 M5 Networks 36
5.1.1 M5 UCaaS 36
5.2 Microsoft Lync UCaaS 37
5.2.1 Solution Analysis 37
5.3 Thinking Phone Networks 37
5.3.1 Solutions 38
5.4 Boundary 38
5.4.1 Solution 39
5.5 enStratus Networks 39
5.5.1 Solutions 39
5.6 RightScale 40
5.6.1 Solutions 40
5.7 Radius Networks 40
5.7.1 Solutions Analysis 40