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

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

$1,995.00

Publication ID
MIC0913004
Publication Date
September 11, 2013
Pages
66
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 revenues will reach $135 billion by the end of 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.
 
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.
 
This report provides an in-depth assessment of the global Big Data market, 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:
  • 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
  • Vendor Assessment & Key Player Profiles: An assessment of the vendor landscape of 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 
 

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 

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: Key Players in the Big Data Market 35

5.1 Vendor Assessment Matrix 35
5.2 Apache Software Foundation 36
5.3 Accenture 36
5.4 Amazon 36
5.5 APTEAN (Formerly CDC Software) 37
5.6 Cisco Systems 37
5.7 Cloudera 37
5.8 Dell 37
5.9 EMC 38
5.10 Facebook 38
5.11 GoodData Corporation 38
5.12 Google 38
5.13 Guavus 39
5.14 Hitachi Data Systems 39
5.15 Hortonworks 39
5.16 HP 40
5.17 IBM 40
5.18 Informatica 40
5.19 Intel 40
5.20 Jaspersoft 41
5.21 Microsoft 41
5.22 MongoDB (Formerly 10Gen) 41
5.23 MU Sigma 42
5.24 Netapp 42
5.25 Opera Solutions 42
5.26 Oracle 42
5.27 Pentaho 43
5.28 Platfora 43
5.29 Qliktech 43
5.30 Quantum 44
5.31 Rackspace 44
5.32 Revolution Analytics 44
5.33 Salesforce 45
5.34 SAP 45
5.35 SAS Institute 45
5.36 Sisense 45
5.37 Software AG/Terracotta 46
5.38 Splunk 46
5.39 Sqrrl 46
5.40 Supermicro 47
5.41 Tableau Software 47
5.42 Teradata 47
5.43 Think Big Analytics 48
5.44 Tidemark Systems 48
5.45 VMware (Part of EMC) 48

6 Chapter 6: Market Analysis 49

6.1 Big Data Revenue: 2014 - 2019 49
6.2 Big Data Revenue by Functional Area: 2014 - 2019 50
6.2.1 Supply Chain Management 51
6.2.2 Business Intelligence 52
6.2.3 Application Infrastructure & Middleware 53
6.2.4 Data Integration Tools & Data Quality Tools 54
6.2.5 Database Management Systems 55
6.2.6 Big Data Social & Content Analytics 56
6.2.7 Big Data Storage Management 57
6.2.8 Big Data Professional Services 58
6.3 Big Data Revenue by Region 2014 - 2019 59
6.3.1 Asia Pacific 60
6.3.2 Eastern Europe 61
6.3.3 Latin & Central America 62
6.3.4 Middle East & Africa 63
6.3.5 North America 64
6.3.6 Western Europe 65
 

List of Figures

Figure 1: The Big Data Value Chain 32
Figure 2: Big Data Vendor Ranking Matrix 2013 35
Figure 3: Big Data Revenue: 2013 – 2019 ($ Million) 49
Figure 4: Big Data Revenue by Functional Area: 2013 – 2019 ($ Million) 50
Figure 5: Big Data Supply Chain Management Revenue: 2013 – 2019 ($ Million) 51
Figure 6: Big Data Supply Business Intelligence Revenue: 2013 – 2019 ($ Million) 52
Figure 7: Big Data Application Infrastructure & Middleware Revenue: 2013 – 2019 ($ Million) 53
Figure 8: Big Data Integration Tools & Data Quality Tools Revenue: 2013 – 2019 ($ Million) 54
Figure 9: Big Data Database Management Systems Revenue: 2013 – 2019 ($ Million) 55
Figure 10: Big Data Social & Content Analytics Revenue: 2013 – 2019 ($ Million) 56
Figure 11: Big Data Storage Management Revenue: 2013 – 2019 ($ Million) 57
Figure 12: Big Data Professional Services Revenue: 2013 – 2019 ($ Million) 58
Figure 13: Big Data Revenue by Region: 2013 – 2019 ($ Million) 59
Figure 14: Asia Pacific Big Data Revenue: 2013 – 2019 ($ Million) 60
Figure 15: Eastern Europe Big Data Revenue: 2013 – 2019 ($ Million) 61
Figure 16: Latin & Central America Big Data Revenue: 2013 – 2019 ($ Million) 62
Figure 17: Middle East & Africa Big Data Revenue: 2013 – 2019 ($ Million) 63
Figure 18: North America Big Data Revenue: 2013 – 2019 ($ Million) 64
Figure 19: Western Europe Big Data Revenue: 2013 – 2019 ($ Million) 65
  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. GoodData Corporation
  19. Google
  20. Guavus
  21. Hitachi Data Systems
  22. Hortonworks
  23. HP
  24. IBM
  25. Informatica
  26. Intel
  27. Jaspersoft
  28. JPMC
  29. McLaren
  30. Microsoft
  31. MongoDB (Formerly 10Gen)
  32. Morgan Stanley
  33. MU Sigma
  34. Netapp
  35. NSA
  36. Opera Solutions
  37. Oracle
  38. Pentaho
  39. Platfora
  40. Qliktech
  41. Quantum
  42. Rackspace
  43. Revolution Analytics
  44. Salesforce
  45. SAP
  46. SAS Institute
  47. Sisense
  48. Software AG/Terracotta
  49. Splunk
  50. Sqrrl
  51. Supermicro
  52. Tableau Software
  53. Teradata
  54. Think Big Analytics
  55. Tidemark Systems
  56. T-Mobile
  57. TomTom
  58. US Xpress
  59. VMware (Part of EMC)
  60. Vodafone
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