Overview:

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 proponents of the Big Data movement. Big Data technologies, and in particular their analytics abilities, offer a multitude of benefits to telecom companies including improved subscriber experience, building and maintaining smarter networks, reducing churn, and generation of new revenue streams.

Mind commerce, thus expects the Big Data driven telecom analytics market 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 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 2013 to 2019.

Topics covered in the report include:

- The Business Case for Big Data: An assessment of the business case, growth drivers and barriers for Big Data
- Big Data Technology: A review of the underlying technologies that resolve big data complexities
- Big Data Use Cases: A review of investments sectors and specific use cases for the Big Data market
- The Big Data Value Chain: An analysis of the value chain of Big Data and the major players involved within it
- Big Data in Telco Analytics: How telecom can utilize Big Data technology to reduce churn, optimize their networks, reduce risks and create new revenue streams
- Telco Case Studies: Case Studies of two major wireless telecom capitalizing on Big Data to reduce churn and improve revenue
- Vendor Assessment & Key Player Profiles: An assessment of the vendor landscape for leading players within the Big Data market
- Market Analysis and Forecasts: A global and regional assessment of the market size and forecasts for the Big Data market from 2014 to 2019

Key Findings:

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.
Mind Commerce has determined that IBM leads the Big Data market in terms of current investments (from a vendor perspective), with estimated revenue for $1.3 Billion in 2012 for its Big Data services, software and hardware sale
Despite challenges such as the lack of clear big data strategies, security concerns and the need for workforce re-skilling, the growth potential of Big Data is unprecedented. Mind Commerce estimates that global spending on Big Data will grow at a CAGR of 48% between 2014 and 2019. Big Data revenues will reach $135 Billion by the end of 2019
Big Data technologies, and in particular their analytics abilities offer a multitude of benefits to telecom including improving subscriber experience, building & maintaining smarter networks, reducing churn and even the generation of new revenue streams
The Big Data driven telecom analytics market 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.

Companies in Report:

Accenture
Adaptive
Adobe
Amazon
Apache Software Foundation
APTEAN (Formerly CDC Software)
BoA
Bristol Myers Squibb
Brooks Brothers
Centre for Economics and Business Research
CIA
Cisco Systems
Cloud Security Alliance (CSA)
Cloudera
Dell
EMC
Facebook
Facebook
GoodData Corporation
Google
Google
Guavus
Hitachi Data Systems
Hortonworks
HP
IBM
Informatica
Intel
Jaspersoft
JPMC
McLaren
Microsoft
MongoDB (Formerly 10Gen)
Morgan Stanley
MU Sigma
Netapp
NSA
Opera Solutions
Oracle
Pentaho
Platfora
Qliktech
Quantum
Rackspace
Revolution Analytics
Salesforce
SAP
SAS Institute
Sisense
Software AG/Terracotta
Splunk
Sqrrl
Supermicro
Tableau Software
Teradata
Think Big Analytics
Tidemark Systems
T-Mobile
TomTom
US Xpress
VMware (Part of EMC)
Vodafone

Target Audience:

Investment Firms
Media Companies
Utilities Companies
Financial Institutions
Application Developers
Government Organizations
Retail & Hospitality Companies
Other Vertical Industry Players
Analytics and Data Reporting Companies
Healthcare Service Providers & Institutions
Fixed and Mobile Telecom service providers
Big Data Technology/Solution (Infrastructure, Software, Service) Vendors

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 and 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 and Giraph 20
2.4 Market Drivers 21
2.4.1 Data Volume and Variety 21
2.4.2 Increasing Adoption of Big Data by Enterprises and 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 and Security: The 'Big' Barrier 22
2.5.2 Workforce Re-skilling and Organizational Resistance 22
2.5.3 Lack of Clear Big Data Strategies 23
2.5.4 Technical Challenges: Scalability and Maintenance 23

3 Chapter 3: Key Investment Sectors for Big Data 24

3.1 Industrial Internet and M2M 24
3.1.1 Big Data in M2M 24
3.1.2 Vertical Opportunities 24
3.2 Retail and Hospitality 25
3.2.1 Improving Accuracy of Forecasts and 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 and 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 and 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 and 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 and Revenue Generation 30
3.9 Other Sectors 31
3.9.1 Aviation: Air Traffic Control 31
3.9.2 Transportation and 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 and Provisioning 33
4.3 Data Warehousing and Business Intelligence 33
4.4 Analytics and Virtualization 33
4.5 Actioning and 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 and Big Data: Identifying and 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 and 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 and Middleware 59
7.2.4 Data Integration Tools and Data Quality Tools 60
7.2.5 Database Management Systems 61
7.2.6 Big Data Social and 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 and Central America 68
7.3.4 Middle East and Africa 69
7.3.5 North America 70
7.3.6 Western Europe 71

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 and Middleware Revenue: 2013 - 2019 ($ Million) 59
Figure 9: Big Data Integration Tools and 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 and 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 and Central America Big Data Revenue: 2013 - 2019 ($ Million) 68
Figure 18: Middle East and 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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