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  3. > Market for Telecom Structured Data, Big Data, and Analytics: Business Case, Analysis and Forecasts 2015 - 2020

Overview:

The telecommunications industry is investing heavily in developing the analytical tools and services to take advantage of both their traditional structured data and unstructured (big) data resources. The goals of each carrier program vary, but share some commonalities including the desire to improve business intelligence gathering, customer care and operations. Carriers are also working diligently to better understand how to monetize data assets, which is often manifest in new products and services at the business-to-business (B2B) level.

This report provides an in-depth assessment of the global Big Data and telecom analytics markets, including a study of the business case, application use cases, vendor landscape, value chain analysis, case studies and a quantitative assessment of the industry from 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:
Telecom network operators
Telecom infrastructure suppliers
Big Data and analytics companies
Data as a Service (DaaS) companies
Cloud-based service providers of all types
Data processing and management companies
Application Programmer Interface (API) companies
Public investment organizations including investment banks
Private investment including hedge funds and private equity

Report Benefits:
Forecasts telecom related Big Data from 2015 to 2020
Understand the emerging need for Big Data mediation
Identify telecom structured data services and solutions
Identify sources of data from next generation applications
Understand unstructured (Big) data systems and solutions
Learn about sources of data in telecom systems and processes
Understand the role and importance of deep packet inspection

Table Of Contents

Market for Telecom Structured Data, Big Data, and Analytics: Business Case, Analysis and Forecasts 2015 - 2020
Table of Contents:

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 and 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 and 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 and 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

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