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

Mind Commerce Publishing
Published Date » 2013-09-12
No. Of Pages » 72
   
 Big Data refers to a massive volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software techniques. While the presence of such datasets is not something new, the past few years have witnessed immense commercial investments in solutions that address the processing and analysis of Big Data. 
   
 Big Data opens a vast array of applications and opportunities in multiple vertical sectors including, but not limited to, retail and hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government, homeland security, and the emerging industrial Internet vertical. 
   
 With access to vast amounts of data sets, telecommunications companies are emerging as major...
Table of Contents: 

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

List of Tables

NA

List of 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

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