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The Big Data Market: Business Case, Market Analysis & Forecasts 2015 - 2020

Published

September 2014

Pages

141

Pricing

Single-user License: $ 1,995 USD
Team License (Up to 5 Users): $ 2,995 USD
Company-wide License: $ 4,995 USD

Keywords

Big Data, Big Data Industry Verticals, Big Data Market Segments, Big Data Forecast, Data Mining, Big Data Telecom, Big Data ICT, Big Data Cloud

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.

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 46% between 2015 and 2020. Big Data revenues will reach almost $190 Billion by the end of 2020.

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 with forecasting from 2015 to 2020.

Topics covered in the report

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
The Business Case for Big Data: An assessment of the business case, growth drivers and barriers for Big Data
Vendor Assessment and 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 2015 to 2020

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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
• Infrastructure, Software, and Service Vendors

Select Findings

• Big Data opens a vast array of applications & opportunities in multiple vertical sectors including not limited to retail & hospitality, media, utilities, financial services, healthcare and pharmaceutical, telecommunications, government & homeland security and the emerging industrial internet vertical. We see certain verticals leading the way in terms of best practices including optimized data collection, analysis, and reporting.
• 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 46% between 2015 and 2020. Big Data revenues will reach $190 Billion by the end of 2020.

Report Benefits

• Detailed forecasts 2015 - 2020
• Learn about Big Data technologies
• Identify leading market segments
• Identify key players and strategies
• Identify opportunities in data analytics
• Understand market drivers and barriers
• Understand the business case for Big Data
• Understand regulatory issues and initiatives

Companies and Organizations in Report

• 1010Data
• Accenture
• Actuate Corporation
• Adaptive
• Adobe
• Amazon
• Apache Software Foundation
• APTEAN (Formerly CDC Software)
• Bank of America
• Bill & Melinda Gates Foundation
• Booz Allen Hamilton
• Bristol Myers Squibb
• Brooks Brothers
• CapGemini
• Carnegie Corporation
• Centre for Economics and Business Research
• CIA
• Cisco Systems
• Cloud Security Alliance (CSA)
• Cloud Standard Customer Council
• Cloudera
• Computer Science Corporation
• DARPA
• Data Direct Network
• Dell
• Deliotte
• EMC
• Facebook
• Fujitsu
• Gartner
• General Electric
• General Electric Capital
• GoodData Corporation
• Google
• Guavus
• Harley Davidson
• Hitachi Data Systems
• Hortonworks
• HP
• IBM
• inBloom
• Informatica
• Intel
• International Standards Organization (ISO)
• International Telecommunications Union (ITU)
• Jaspersoft
• JP Morgan Chase
• Juniper Networks
• MarkLogic
• McLaren Racing Team
• Microsoft
• MongoDB (Formerly 10Gen)
• Morgan Stanley
• MU Sigma
• Netapp
• New Classrooms Innovation Partners
• National Institute of Standards & Technology (NIST)
• NSA
• OASIS
• Open Data Center Alliance
• Open Data Foundation (ODaF)
• Opera Solutions
• Oracle
• Pentaho
• Platfora
• Qliktech
• Quantum
• Rackspace
• Raytheon
• Renaissance Learning
• Revolution Analytics
• Rockwell Automation
• Salesforce
• SAP
• SAS Institute
• Sherwin Williams
• Siemans
• Sisense
• Software AG/Terracotta
• Splunk
• Sqrrl
• Supermicro
• Tableau Software
• Tata Consultancy Services
• Teradata
• Think Big Analytics
• TIBCO
• Tidemark Systems
• T-Mobile
• TomTom
• Twitter
• US Federal Government (various agencies and departments)
• US Xpress
• VMware (Part of EMC)
• Vodafone
• Wipro
• Zettics

Table of Contents

1 Introduction 10
1.1 Executive Summary 10
1.2 Topics Covered 12
1.3 Key Findings 13
1.4 Target Audience 14
1.5 Companies Mentioned 15
2 Big Data Technology & Business Case 20
2.1 Defining Big Data 20
2.2 Key Characteristics of Big Data 21
2.2.1 Volume 21
2.2.2 Variety 22
2.2.3 Velocity 22
2.2.4 Variability 23
2.2.5 Complexity 23
2.3 Big Data Technology 24
2.3.1 Hadoop 24
2.3.2 Other Apache Projects 26
2.3.3 NoSQL 26
2.3.3.1 Hbase 27
2.3.3.2 Cassandra 27
2.3.3.3 Mongo DB 28
2.3.3.4 Riak 28
2.3.3.5 CouchDB 28
2.3.4 MPP Databases 28
2.3.5 Others and Emerging Technologies 29
2.3.5.1 Storm 29
2.3.5.2 Drill 29
2.3.5.3 Dremel 29
2.3.5.4 SAP HANA 29
2.3.5.5 Gremlin & Giraph 30
2.3.6 New Paradigms and Techniques 30
2.3.6.1 Streaming Analytics 30
2.3.6.2 Cloud Technology 30
2.3.6.3 Google Search 30
2.3.6.4 Customize Analytical Tools 31
2.3.6.5 Internet Keywords 31
2.3.6.6 Gamification 32
2.4 Big Data Roadmap 34
2.5 Market Drivers 36
2.5.1 Data Volume & Variety 36
2.5.2 Increasing Adoption of Big Data by Enterprises and Telecom 36
2.5.3 Maturation of Big Data Software 36
2.5.4 Continued Investments in Big Data by Web Giants 36
2.5.5 Business Drivers 37
2.6 Market Barriers 38
2.6.1 Privacy and Security: The 'Big' Barrier 38
2.6.2 Workforce Re-skilling and Organizational Resistance 38
2.6.3 Lack of Clear Big Data Strategies 39
2.6.4 Technical Challenges: Scalability & Maintenance 39
2.6.5 Big Data Development Expertise 39
3 Key Investment Sectors for Big Data 40
3.1 Industrial Internet and Machine-to-Machine 40
3.1.1 Big Data in M2M 40
3.1.2 Vertical Opportunities 40
3.2 Retail and Hospitality 40
3.2.1 Improving Accuracy of Forecasts & Stock Management 41
3.2.2 Determining Buying Patterns 41
3.2.3 Hospitality Use Cases 41
3.2.4 Personalized Marketing 42
3.3 Media 44
3.3.1 Social Media 44
3.3.2 Social Gaming Analytics 44
3.3.3 Usage of Social Media Analytics by Other Verticals 45
3.3.4 Internet Keyword Search 45
3.4 Utilities 47
3.4.1 Analysis of Operational Data 47
3.4.2 Application Areas for the Future 47
3.5 Financial Services 48
3.5.1 Fraud Analysis, Mitigation & Risk Profiling 48
3.5.2 Merchant-Funded Reward Programs 48
3.5.3 Customer Segmentation 48
3.5.4 Customer Retention & Personalized Product Offering 48
3.5.5 Insurance Companies 50
3.6 Healthcare and Pharmaceutical 50
3.6.1 Drug Development 50
3.6.2 Medical Data Analytics 50
3.6.3 Case Study: Identifying Heartbeat Patterns 50
3.7 Telecommunications 51
3.7.1 Telco Analytics: Customer/Usage Profiling and Service Optimization 51
3.7.2 Big Data Analytic Tools 51
3.7.3 Speech Analytics 52
3.7.4 New Products and Services 52
3.8 Government and Homeland Security 53
3.8.1 Big Data Research 53
3.8.2 Statistical Analysis 55
3.8.3 Language Translation 55
3.8.4 Developing New Applications for the Public 56
3.8.5 Tracking Crime 56
3.8.6 Intelligence Gathering 56
3.8.7 Fraud Detection & Revenue Generation 56
3.9 Other Sectors 58
3.9.1 Aviation 58
3.9.2 Transportation & Logistics: Optimizing Fleet Usage 58
3.9.3 Sports: Real-Time Processing of Statistics 59
3.9.4 Education 59
3.9.5 Manufacturing 60
4 The Big Data Value Chain 66
4.1 How Fragmented is the Big Data Value Chain? 66
4.2 Data Acquisitioning & Provisioning 67
4.3 Data Warehousing & Business Intelligence 67
4.4 Analytics & Virtualization 67
4.5 Actioning and Business Process Management 68
4.6 Data Governance 68
5 Big Data Analytics 69
5.1 What is Big Data Analytics? 69
5.2 The Importance of Big Data Analytics 70
5.3 Reactive vs. Proactive Analytics 71
5.4 Technology and Implementation Approaches 73
5.4.1 Grid Computing 73
5.4.2 In-Database processing 73
5.4.3 In-Memory Analytics 75
5.4.4 Data Mining 75
5.4.5 Predictive Analytics 77
5.4.6 Natural Language Processing 80
5.4.7 Text Analytics 84
5.4.8 Visual Analytics 85
5.4.9 Association rule learning 86
5.4.10 Classification tree analysis 87
5.4.11 Machine Learning 87
5.4.11.1 Neural networks 88
5.4.11.2 Multilayer Perceptron (MLP) 89
5.4.11.3 Radial Basis Functions 90
5.4.11.4 Support vector machines 90
5.4.11.5 Naïve Bayes 90
5.4.11.6 k-nearest neighbors 91
5.4.11.7 Geospatial predictive modelling 92
5.4.12 Regression Analysis 92
5.4.13 Social Network Analysis 93
6 Standardization and Regulatory Initiatives 94
6.1 Cloud Standards Customer Council - Big Data Working Group 94
6.2 National Institute of Standards and Technology - Big Data Working Group 95
6.3 OASIS 96
6.4 Open Data Foundation 98
6.5 Open Data Center Alliance 99
6.6 Cloud Security Alliance - Big Data Working Group 100
6.7 International Telecommunications Union 101
6.8 International Organization for Standardization 101
6.9 International Organization for Standardization) 101
7 Key Players in the Big Data Market 102
7.1 Vendor Assessment Matrix 102
7.2 1010Data 102
7.3 Actuate Corporation 103
7.4 Accenture 103
7.5 Amazon 103
7.6 Apache Software Foundation 104
7.7 APTEAN (Formerly CDC Software) 104
7.8 Booz Allen Hamilton 104
7.9 Cap Gemini 105
7.10 Cisco Systems 105
7.11 Cloudera 105
7.12 Computer Science Corporation 105
7.13 DataDirect Network 106
7.14 Dell 107
7.15 Deloitte 107
7.16 EMC 107
7.17 Facebook 107
7.18 Fujitsu 108
7.19 General Electric 109
7.20 GoodData Corporation 110
7.21 Google 110
7.22 Guavus 110
7.23 Hitachi Data Systems 111
7.24 Hortonworks 111
7.25 HP 111
7.26 IBM 112
7.27 Informatica 112
7.28 Intel 112
7.29 Jaspersoft 112
7.30 Juniper Networks 113
7.31 Marklogic 113
7.32 Microsoft 114
7.33 MongoDB (Formerly 10Gen) 114
7.34 MU Sigma 114
7.35 Netapp 115
7.36 NTT Data 115
7.37 Opera Solutions 116
7.38 Oracle 116
7.39 Pentaho 116
7.40 Platfora 116
7.41 Qliktech 117
7.42 Quantum 117
7.43 Rackspace 117
7.44 Revolution Analytics 117
7.45 Salesforce 118
7.46 SAP 118
7.47 SAS Institute 118
7.48 Sisense 119
7.49 Software AG/Terracotta 119
7.50 Splunk 119
7.51 Sqrrl 120
7.52 Supermicro 120
7.53 Tableau Software 120
7.54 Tata Consultancy Services 121
7.55 Teradata 121
7.56 Think Big Analytics 121
7.57 TIBCO 121
7.58 Tidemark Systems 122
7.59 VMware (Part of EMC) 122
7.60 Wipro 122
7.61 Zettics 123
8 Market Analysis 124
8.1 Big Data Revenue 2014 - 2020 124
8.2 Big Data Revenue by Functional Area 2014 - 2020 125
8.2.1 Supply Chain Management 126
8.2.2 Business Intelligence 127
8.2.3 Application Infrastructure & Middleware 128
8.2.4 Data Integration Tools & Data Quality Tools 129
8.2.5 Database Management Systems 130
8.2.6 Big Data Social & Content Analytics 131
8.2.7 Big Data Storage Management 132
8.2.8 Big Data Professional Services 133
8.3 Big Data Revenue by Region 2014 - 2020 134
8.3.1 Asia Pacific 135
8.3.2 Eastern Europe 136
8.3.3 Latin & Central America 137
8.3.4 Middle East & Africa 138
8.3.5 North America 139
8.3.6 Western Europe 140

Figures

Figure 1: NoSQL vs Legacy DB Performance Comparisons 27
Figure 2: 2014 Gartner Hype Cycle for Emerging Technologies 34
Figure 3: Roadmap Big Data Technologies 2014 - 2030 35
Figure 4: The Big Data Value Chain 66
Figure 5: Big Data Vendor Ranking Matrix 102
Figure 6: Big Data Revenue 2013 - 2020 124
Figure 7: Big Data Revenue by Functional Area 2013 - 2020 125
Figure 8: Big Data Supply Chain Management Revenue 2013 - 2020 126
Figure 9: Big Data Supply Business Intelligence Revenue 2013 - 2020 127
Figure 10: Big Data Application Infrastructure & Middleware Revenue 2013 - 2020 128
Figure 11: Big Data Integration and Quality Tools Revenue 2013 - 2020 129
Figure 12: Big Data DB Management Systems Revenue 2013 - 2020 130
Figure 13: Big Data Social & Content Analytics Revenue 2013 - 2020 131
Figure 14: Big Data Storage Management Revenue 2013 - 2020 132
Figure 15: Big Data Professional Services Revenue 2013 - 2020 133
Figure 16: Big Data Revenue by Region 2013 - 2020 134
Figure 17: Asia Pacific Big Data Revenue 2013 - 2020 135
Figure 18: Eastern Europe Big Data Revenue 2013 - 2020 136
Figure 19: Latin & Central America Big Data Revenue 2013 - 2020 137
Figure 20: Middle East & Africa Big Data Revenue 2013 - 2020 138
Figure 21: North America Big Data Revenue 2013 - 2020 139
Figure 22: Western Europe Big Data Revenue 2013 - 2020 140