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



Published: September 2013
Pages: 72

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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
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
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
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

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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Data and Analytics

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