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

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

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Publication ID
MIC0913007
Publication Date
September 16, 2013
Pages
72
Regions Covered
Publisher
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 and telecom analytics market is expected to reach $5.4 billion by 2019.
 
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.
 

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

 

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 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
  1. Accenture
  2. Adaptive
  3. Adobe
  4. Amazon
  5. Apache Software Foundation
  6. APTEAN (Formerly CDC Software)
  7. BoA
  8. Bristol Myers Squibb
  9. Brooks Brothers
  10. Centre for Economics and Business Research
  11. CIA
  12. Cisco Systems
  13. Cloud Security Alliance (CSA)
  14. Cloudera
  15. Dell
  16. EMC
  17. Facebook
  18. Facebook
  19. GoodData Corporation
  20. Google
  21. Google
  22. Guavus
  23. Hitachi Data Systems
  24. Hortonworks
  25. HP
  26. IBM
  27. Informatica
  28. Intel
  29. Jaspersoft
  30. JPMC
  31. McLaren
  32. Microsoft
  33. MongoDB (Formerly 10Gen)
  34. Morgan Stanley
  35. MU Sigma
  36. Netapp
  37. NSA
  38. Opera Solutions
  39. Oracle
  40. Pentaho
  41. Platfora
  42. Qliktech
  43. Quantum
  44. Rackspace
  45. Revolution Analytics
  46. Salesforce
  47. SAP
  48. SAS Institute
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