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The Big Data Market: 2015 – 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

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Date: 25-May-2015
No. of pages: 351
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“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.

Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to scientific R&D.

Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for nearly $40 Billion in 2015 alone. These investments are further expected to grow at a CAGR of 14% over the next 5 years.

The “Big Data Market: 2015 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2015 through to 2030. Historical figures are also presented for 2010, 2011, 2012, 2013 and 2014. The forecasts are further segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and 35 countries.

The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.

The report covers the following topics:


  • Big Data ecosystem

  • Market drivers and barriers

  • Big Data technology, standardization and regulatory initiatives

  • Big Data industry roadmap and value chain

  • Analysis and use cases for 15 vertical markets

  • Big Data analytics technology and case studies

  • Big Data vendor market share

  • Company profiles and strategies of 140 Big Data ecosystem players

  • Strategic recommendations for Big Data hardware, software and professional services vendors and enterprises

  • Market analysis and forecasts from 2015 till 2030


Historical Revenue & Forecast Segmentation

Market forecasts and historical revenue figures are provided for each of the following submarkets and their subcategories:


  • Hardware, Software & Professional Services

  • Hardware

  • Software

  • Professional Services


Horizontal Submarkets

  • Storage & Compute Infrastructure

  • Networking Infrastructure

  • Hadoop & Infrastructure Software

  • SQL

  • NoSQL

  • Analytic Platforms & Applications

  • Cloud Platforms

  • Professional Services


Vertical Submarkets

  • Automotive, Aerospace & Transportation

  • Banking & Securities

  • Defense & Intelligence

  • Education

  • Healthcare & Pharmaceutical

  • Smart Cities & Intelligent Buildings

  • Insurance

  • Manufacturing & Natural Resources

  • Web, Media & Entertainment

  • Public Safety & Homeland Security

  • Public Services

  • Retail & Hospitality

  • Telecommunications

  • Utilities & Energy

  • Wholesale Trade

  • Others


Regional Markets

  • Asia Pacific

  • Eastern Europe

  • Latin & Central America

  • Middle East & Africa

  • North America

  • Western Europe


Country Markets

Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany,  India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK,  USA

The report provides answers to the following key questions:


  • How big is the Big Data ecosystem?

  • How is the ecosystem evolving by segment and region?

  • What will the market size be in 2020 and at what rate will it grow?

  • What trends, challenges and barriers are influencing its growth?

  • Who are the key Big Data software, hardware and services vendors and what are their strategies?

  • How much are vertical enterprises investing in Big Data?

  • What opportunities exist for Big Data analytics?

  • Which countries and verticals will see the highest percentage of Big Data investments?


Key Findings

The report has the following key findings:


  • In 2015, Big Data vendors will pocket nearly $40 Billion from hardware, software and professional services revenues

  • Big Data investments are further expected to grow at a CAGR of 14% over the next 5 years, eventually accounting for nearly $80 Billion by the end of 2020

  • The market is ripe for acquisitions of pure-play Big Data startups, as competition heats up between IT incumbents

  • Nearly every large scale IT vendor maintains a Big Data portfolio

  • At present, the market is largely dominated by hardware sales and professional services in terms of revenue

  • Going forward, software vendors, particularly those in the Big Data analytics segment, are expected to significantly increase their stake in the Big Data market

  • By the end of 2020, SNS Research expects Big Data software revenue to exceed hardware investments by nearly $8 Billion


List of Companies Mentioned

  • 1010data

  • Accel Partners

  • Accenture

  • Actian Corporation

  • Actuate Corporation

  • Adaptive Insights

  • adMarketplace

  • Adobe

  • ADP

  • Advizor Solutions

  • AeroSpike

  • AFS Technologies

  • AlchemyDB

  • Aldeasa

  • Alpine Data Labs

  • Alteryx

  • Altiscale

  • Altosoft

  • Amazon.com

  • AMD

  • AnalyticsIQ

  • Antic Entertainment

  • Antivia

  • AOL

  • Apple

  • AppNexus

  • Arcplan

  • Ascendas

  • AT&T

  • Attivio

  • Automated Insights

  • AutoZone

  • Avvasi

  • AWS (Amazon Web Services)

  • Axiata Group

  • Ayasdi

  • Bank of America

  • Basho

  • Beeline Kazakhstan

  • Betfair

  • BeyondCore

  • Birst

  • Bitam

  • BlueKai

  • Bluelock

  • BMC Software

  • BMW

  • Board International

  • Boeing

  • Booz Allen Hamilton

  • Box, Inc.

  • Buffalo Studios

  • BurstaBit

  • CaixaTarragona

  • Capgemini

  • Cellwize

  • Centrifuge Systems

  • CenturyLink

  • Chang

  • Chartio

  • China Telecom

  • CIA (Central Intelligence Agency)

  • Cisco Systems

  • Citywire

  • ClearStory Data

  • Cloudera

  • Coca-Cola

  • Comptel

  • Concur

  • Concurrent

  • Contexti

  • Coriant

  • Couchbase

  • CSA (Cloud Security Alliance)

  • CSC (Computer Science Corporation)

  • CSCC (Cloud Standards Customer Council)

  • DataHero

  • Datameer

  • DataRPM

  • DataStax

  • Datawatch Corporation

  • DDN (DataDirect Network)

  • Decisyon

  • Dell

  • Deloitte

  • Delta

  • Denodo Technologies

  • Department of Commerce

  • Deutsche Bank

  • Deutsche Telekom

  • Digital Reasoning

  • Dimensional Insight

  • Dollar General

  • Domo

  • Dotomi

  • Dundas Data Visualization

  • eBay

  • El Corte Inglés

  • Electronic Arts

  • Eligotech

  • EMC Corporation

  • Engineering Group (Engineering Ingegneria Informatica)

  • eQ Technologic

  • Equifax

  • Ericsson

  • Ernst & Young

  • E-Touch

  • European Space Agency

  • eXelate

  • Experian

  • Facebook

  • FedEx

  • Ferguson

  • FICO

  • Ford

  • Fractal Analytics

  • Fujitsu

  • Fusion-io

  • Gamegos

  • Ganz

  • GE (General Electric)

  • Goldman Sachs

  • GoodData Corporation

  • Google

  • Greylock Partners

  • GTRI (Georgia Tech Research Institute)

  • Guavus

  • Hadapt

  • HDS (Hitachi Data Systems)

  • Hortonworks

  • HP

  • Hyve Solutions

  • IBM

  • iDashboards

  • IEC (International Electrotechnical Commission)

  • Ignition Partners

  • Incorta

  • InetSoft Technology Corporation

  • InfiniDB

  • Infobright

  • Infor

  • Informatica Corporation

  • Information Builders

  • In-Q-Tel

  • Intel

  • Internap Network Services Corporation

  • Intucell

  • Inversis Banco

  • ISO (International Organization for Standardization)

  • ITT Corporation

  • ITU (International Telecommunications Union)

  • J.P. Morgan

  • Jaspersoft

  • Jedox

  • Jinfonet Software

  • Johnson & Johnson

  • JP Morgan

  • Juguettos

  • Juniper Networks

  • Kabam

  • Karmasphere

  • KDDI

  • Kixeye

  • Knime

  • Kobo

  • Kofax

  • Kognitio

  • KPMG

  • KT (Korea Telecom)

  • L-3 Communications

  • L-3 Data Tactics

  • Lavastorm Analytics

  • LG CNS

  • LinkedIn

  • Logi Analytics

  • Looker Data Sciences

  • LucidWorks

  • Mahindra Satyam

  • Manthan Software Services

  • MapR

  • MarkLogic

  • Marriott International

  • Mayfield fund

  • McDonnell Ventures

  • McGraw Hill Education

  • MediaMind

  • MemSQL

  • Meritech Capital Partners

  • Microsoft

  • MicroStrategy

  • mig33

  • MongoDB

  • MongoDB (Formerly 10gen)

  • Motorola

  • Movistar

  • Mu Sigma

  • Myrrix

  • Nami Media

  • Navteq

  • Neo Technology

  • NetApp

  • NetFlix

  • Nexon

  • NIST (National Institute of Standards and Technology)

  • North Bridge

  • NTT Data

  • NTT DoCoMo

  • NYSE (New York Stock Exchange)

  • OASIS

  • ODaF (Open Data Foundation)

  • Open Data Center Alliance

  • OpenText Corporation

  • Opera Solutions

  • Oracle

  • Orange

  • Orbitz

  • Palantir Technologies

  • Panorama Software

  • ParAccel

  • ParStream

  • Pentaho

  • Pervasive Software

  • Phocas

  • Pivotal Software

  • Platfora

  • Playtika

  • Pokemon

  • Proctor and Gamble

  • Prognoz

  • Pronovias

  • PwC

  • Pyramid Analytics

  • Qlik

  • Quantum Corporation

  • Qubole

  • Quiterian

  • Rackspace

  • RainStor

  • RapidMiner

  • Recorded Future

  • Relational Technology

  • Renault

  • ReNet Tecnologia

  • Rentrak

  • Revolution Analytics

  • RiteAid

  • RJMetrics

  • Robi Axiata

  • Royal Dutch Shell

  • Sabre

  • Sailthru

  • Sain Engineering

  • Salesforce.com

  • Salient Management Company

  • Samsung

  • SAP

  • SAS Institute

  • Savvis

  • Scoreloop

  • Seagate Technology

  • SGI

  • Shuffle Master

  • Simba Technologies

  • SiSense

  • Skyscanner

  • SmugMug

  • Snapdeal

  • Software AG

  • Sojo Studios

  • SolveDirect

  • Sony

  • Southern States Cooperative

  • SpagoBI Labs

  • Splice Machine

  • Splunk

  • Spotfire

  • Spotme

  • Sqrrl

  • Starbucks

  • Strategy Companion

  • Supermicro

  • SynerScope

  • Tableau Software

  • Talend

  • Tango

  • TapJoy

  • Targit

  • TCS (Tata Consultancy Services)

  • Telefónica

  • Tencent

  • Teradata

  • Terracotta

  • Terremark

  • The Hut Group

  • The Knot

  • The Ladders

  • The Trade Desk

  • Think Big Analytics

  • Thomson Reuters

  • ThoughtSpot

  • TIBCO Software

  • Tidemark

  • TubeMogul

  • Tunewiki

  • U.S. Air Force

  • U.S. Army

  • U.S. Navy

  • Ubiquisys

  • UBS

  • Umami TV

  • UN (United Nations)

  • Unilever

  • US Xpress

  • Venture Partners

  • Verizon

  • Versant

  • Vertica

  • VIMPELCOM

  • Vmware

  • VNG

  • Vodafone

  • Volkswagen

  • Walt Disney Company

  • WIND Mobile

  • WiPro

  • Xclaim

  • Xyratex

  • Yael Software

  • Yellowfin International

  • Zettics

  • Zoomdata

  • Zucchetti

  • Zynga


Countires Covered

  • Argentina

  • Australia

  • Brazil

  • Canada

  • China

  • Czech Republic

  • Denmark

  • Finland

  • France

  • Germany

  • India

  • Indonesia

  • Israel

  • Italy

  • Japan

  • Malaysia

  • Mexico

  • Netherlands

  • Norway

  • Pakistan

  • Philippines

  • Poland

  • Qatar

  • Russia

  • Saudi Arabia

  • Singapore

  • South Africa

  • South Korea

  • Spain

  • Sweden

  • Taiwan

  • Thailand

  • UAE

  • UK

  • USA

The Big Data Market: 2015 – 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

Table of Contents

1 Chapter 1: Introduction 18
1.1 Executive Summary 18
1.2 Topics Covered 20
1.3 Historical Revenue & Forecast Segmentation 21
1.4 Key Questions Answered 23
1.5 Key Findings 24
1.6 Methodology 25
1.7 Target Audience 26
1.8 Companies & Organizations Mentioned 27

2 Chapter 2: An Overview of Big Data 31
2.1 What is Big Data? 31
2.2 Key Approaches to Big Data Processing 31
2.2.1 Hadoop 32
2.2.2 NoSQL 33
2.2.3 MPAD (Massively Parallel Analytic Databases) 33
2.2.4 In-memory Processing 34
2.2.5 Stream Processing Technologies 34
2.2.6 Spark 35
2.2.7 Other Databases & Analytic Technologies 35
2.3 Key Characteristics of Big Data 36
2.3.1 Volume 36
2.3.2 Velocity 36
2.3.3 Variety 36
2.3.4 Value 37
2.4 Market Growth Drivers 38
2.4.1 Awareness of Benefits 38
2.4.2 Maturation of Big Data Platforms 38
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 39
2.4.4 Growth of Data Volume, Velocity & Variety 39
2.4.5 Vendor Commitments & Partnerships 39
2.4.6 Technology Trends Lowering Entry Barriers 40
2.5 Market Barriers 40
2.5.1 Lack of Analytic Specialists 40
2.5.2 Uncertain Big Data Strategies 40
2.5.3 Organizational Resistance to Big Data Adoption 41
2.5.4 Technical Challenges: Scalability & Maintenance 41
2.5.5 Security & Privacy Concerns 41

3 Chapter 3: Vertical Opportunities & Use Cases for Big Data 43
3.1 Automotive, Aerospace & Transportation 43
3.1.1 Predictive Warranty Analysis 43
3.1.2 Predictive Aircraft Maintenance & Fuel Optimization 44
3.1.3 Air Traffic Control 44
3.1.4 Transport Fleet Optimization 44
3.2 Banking & Securities 46
3.2.1 Customer Retention & Personalized Product Offering 46
3.2.2 Risk Management 46
3.2.3 Fraud Detection 46
3.2.4 Credit Scoring 47
3.3 Defense & Intelligence 48
3.3.1 Intelligence Gathering 48
3.3.2 Energy Saving Opportunities in the Battlefield 48
3.3.3 Preventing Injuries on the Battlefield 49
3.4 Education 50
3.4.1 Information Integration 50
3.4.2 Identifying Learning Patterns 50
3.4.3 Enabling Student-Directed Learning 50
3.5 Healthcare & Pharmaceutical 52
3.5.1 Managing Population Health Efficiently 52
3.5.2 Improving Patient Care with Medical Data Analytics 52
3.5.3 Improving Clinical Development & Trials 52
3.5.4 Improving Time to Market 53
3.6 Smart Cities & Intelligent Buildings 54
3.6.1 Energy Optimization & Fault Detection 54
3.6.2 Intelligent Building Analytics 54
3.6.3 Urban Transportation Management 55
3.6.4 Optimizing Energy Production 55
3.6.5 Water Management 55
3.6.6 Urban Waste Management 55
3.7 Insurance 57
3.7.1 Claims Fraud Mitigation 57
3.7.2 Customer Retention & Profiling 57
3.7.3 Risk Management 58
3.8 Manufacturing & Natural Resources 59
3.8.1 Asset Maintenance & Downtime Reduction 59
3.8.2 Quality & Environmental Impact Control 59
3.8.3 Optimized Supply Chain 59
3.8.4 Exploration & Identification of Wells & Mines 60
3.8.5 Maximizing the Potential of Drilling 60
3.8.6 Production Optimization 60
3.9 Web, Media & Entertainment 61
3.9.1 Audience & Advertising Optimization 61
3.9.2 Channel Optimization 61
3.9.3 Recommendation Engines 61
3.9.4 Optimized Search 62
3.9.5 Live Sports Event Analytics 62
3.9.6 Outsourcing Big Data Analytics to Other Verticals 62
3.10 Public Safety & Homeland Security 63
3.10.1 Cyber Crime Mitigation 63
3.10.2 Crime Prediction Analytics 63
3.10.3 Video Analytics & Situational Awareness 63
3.11 Public Services 65
3.11.1 Public Sentiment Analysis 65
3.11.2 Fraud Detection & Prevention 65
3.11.3 Economic Analysis 65
3.12 Retail & Hospitality 66
3.12.1 Customer Sentiment Analysis 66
3.12.2 Customer & Branch Segmentation 66
3.12.3 Price Optimization 66
3.12.4 Personalized Marketing 67
3.12.5 Optimized Supply Chain 67
3.13 Telecommunications 68
3.13.1 Network Performance & Coverage Optimization 68
3.13.2 Customer Churn Prevention 68
3.13.3 Personalized Marketing 68
3.13.4 Location Based Services 69
3.13.5 Fraud Detection 69
3.14 Utilities & Energy 70
3.14.1 Customer Retention 70
3.14.2 Forecasting Energy 70
3.14.3 Billing Analytics 70
3.14.4 Predictive Maintenance 70
3.14.5 Turbine Placement Optimization 71
3.15 Wholesale Trade 72
3.15.1 In-field Sales Analytics 72
3.15.2 Monitoring the Supply Chain 72

4 Chapter 4: Big Data Industry Roadmap & Value Chain 73
4.1 Big Data Industry Roadmap 73
4.1.1 2010 – 2013: Initial Hype and the Rise of Analytics 73
4.1.2 2014 – 2017: Emergence of SaaS Based Big Data Solutions 74
4.1.3 2018 – 2020: Growing Adoption of Scalable Machine Learning 75
4.1.4 2021 & Beyond: Widespread Investments on Cognitive & Personalized Analytics 75
4.2 The Big Data Value Chain 76
4.2.1 Hardware Providers 76
4.2.1.1 Storage & Compute Infrastructure Providers 76
4.2.1.2 Networking Infrastructure Providers 77
4.2.2 Software Providers 78
4.2.2.1 Hadoop & Infrastructure Software Providers 78
4.2.2.2 SQL & NoSQL Providers 78
4.2.2.3 Analytic Platform & Application Software Providers 78
4.2.2.4 Cloud Platform Providers 79
4.2.3 Professional Services Providers 79
4.2.4 End-to-End Solution Providers 79
4.2.5 Vertical Enterprises 79

5 Chapter 5: Big Data Analytics 80
5.1 What are Big Data Analytics? 80
5.2 The Importance of Analytics 80
5.3 Reactive vs. Proactive Analytics 81
5.4 Customer vs. Operational Analytics 82
5.5 Technology & Implementation Approaches 82
5.5.1 Grid Computing 82
5.5.2 In-Database Processing 83
5.5.3 In-Memory Analytics 83
5.5.4 Machine Learning & Data Mining 83
5.5.5 Predictive Analytics 84
5.5.6 NLP (Natural Language Processing) 84
5.5.7 Text Analytics 85
5.5.8 Visual Analytics 86
5.5.9 Social Media, IT & Telco Network Analytics 86
5.6 Vertical Market Case Studies 87
5.6.1 Amazon – Delivering Cloud Based Big Data Analytics 87
5.6.2 Facebook – Using Analytics to Monetize Users with Advertising 87
5.6.3 WIND Mobile – Using Analytics to Monitor Video Quality 88
5.6.4 Coriant Analytics Services – SaaS Based Big Data Analytics for Telcos 88
5.6.5 Boeing – Analytics for the Battlefield 89
5.6.6 The Walt Disney Company – Utilizing Big Data and Analytics in Theme Parks 89

6 Chapter 6: Standardization & Regulatory Initiatives 91
6.1 CSCC (Cloud Standards Customer Council) – Big Data Working Group 91
6.2 NIST (National Institute of Standards and Technology) – Big Data Working Group 92
6.3 OASIS –Technical Committees 93
6.4 ODaF (Open Data Foundation) 94
6.5 Open Data Center Alliance 94
6.6 CSA (Cloud Security Alliance) – Big Data Working Group 95
6.7 ITU (International Telecommunications Union) 96
6.8 ISO (International Organization for Standardization) and Others 96

7 Chapter 7: Market Analysis & Forecasts 97
7.1 Global Outlook of the Big Data Market 97
7.2 Submarket Segmentation 98
7.2.1 Storage and Compute Infrastructure 99
7.2.2 Networking Infrastructure 100
7.2.3 Hadoop & Infrastructure Software 101
7.2.4 SQL 102
7.2.5 NoSQL 103
7.2.6 Analytic Platforms & Applications 104
7.2.7 Cloud Platforms 105
7.2.8 Professional Services 106
7.3 Vertical Market Segmentation 107
7.3.1 Automotive, Aerospace & Transportation 108
7.3.2 Banking & Securities 109
7.3.3 Defense & Intelligence 110
7.3.4 Education 111
7.3.5 Healthcare & Pharmaceutical 112
7.3.6 Smart Cities & Intelligent Buildings 113
7.3.7 Insurance 114
7.3.8 Manufacturing & Natural Resources 115
7.3.9 Media & Entertainment 116
7.3.10 Public Safety & Homeland Security 117
7.3.11 Public Services 118
7.3.12 Retail & Hospitality 119
7.3.13 Telecommunications 120
7.3.14 Utilities & Energy 121
7.3.15 Wholesale Trade 122
7.3.16 Other Sectors 123
7.4 Regional Outlook 124
7.5 Asia Pacific 125
7.5.1 Country Level Segmentation 126
7.5.2 Australia 127
7.5.3 China 128
7.5.4 India 129
7.5.5 Indonesia 130
7.5.6 Japan 131
7.5.7 Malaysia 132
7.5.8 Pakistan 133
7.5.9 Philippines 134
7.5.10 Singapore 135
7.5.11 South Korea 136
7.5.12 Taiwan 137
7.5.13 Thailand 138
7.5.14 Rest of Asia Pacific 139
7.6 Eastern Europe 140
7.6.1 Country Level Segmentation 141
7.6.2 Czech Republic 142
7.6.3 Poland 143
7.6.4 Russia 144
7.6.5 Rest of Eastern Europe 145
7.7 Latin & Central America 146
7.7.1 Country Level Segmentation 147
7.7.2 Argentina 148
7.7.3 Brazil 149
7.7.4 Mexico 150
7.7.5 Rest of Latin & Central America 151
7.8 Middle East & Africa 152
7.8.1 Country Level Segmentation 153
7.8.2 Israel 154
7.8.3 Qatar 155
7.8.4 Saudi Arabia 156
7.8.5 South Africa 157
7.8.6 UAE 158
7.8.7 Rest of the Middle East & Africa 159
7.9 North America 160
7.9.1 Country Level Segmentation 161
7.9.2 Canada 162
7.9.3 USA 163
7.10 Western Europe 164
7.10.1 Country Level Segmentation 165
7.10.2 Denmark 166
7.10.3 Finland 167
7.10.4 France 168
7.10.5 Germany 169
7.10.6 Italy 170
7.10.7 Netherlands 171
7.10.8 Norway 172
7.10.9 Spain 173
7.10.10 Sweden 174
7.10.11 UK 175
7.10.12 Rest of Western Europe 176

8 Chapter 8: Vendor Landscape 177
8.1 1010data 177
8.2 Accenture 179
8.3 Actian Corporation 181
8.4 Actuate Corporation 183
8.5 Adaptive Insights 185
8.6 Advizor Solutions 186
8.7 AeroSpike 187
8.8 AFS Technologies 189
8.9 Alpine Data Labs 190
8.10 Alteryx 191
8.11 Altiscale 193
8.12 Antivia 194
8.13 Arcplan 195
8.14 Attivio 196
8.15 Automated Insights 198
8.16 AWS (Amazon Web Services) 199
8.17 Ayasdi 201
8.18 Basho 202
8.19 BeyondCore 204
8.20 Birst 205
8.21 Bitam 206
8.22 Board International 207
8.23 Booz Allen Hamilton 208
8.24 Capgemini 210
8.25 Cellwize 212
8.26 Centrifuge Systems 213
8.27 CenturyLink 214
8.28 Chartio 215
8.29 Cisco Systems 216
8.30 ClearStory Data 218
8.31 Cloudera 219
8.32 Comptel 221
8.33 Concurrent 223
8.34 Contexti 224
8.35 Couchbase 225
8.36 CSC (Computer Science Corporation) 227
8.37 DataHero 228
8.38 Datameer 229
8.39 DataRPM 230
8.40 DataStax 231
8.41 Datawatch Corporation 232
8.42 DDN (DataDirect Network) 233
8.43 Decisyon 234
8.44 Dell 235
8.45 Deloitte 237
8.46 Denodo Technologies 238
8.47 Digital Reasoning 239
8.48 Dimensional Insight 240
8.49 Domo 241
8.50 Dundas Data Visualization 242
8.51 Eligotech 243
8.52 EMC Corporation 244
8.53 Engineering Group (Engineering Ingegneria Informatica) 245
8.54 eQ Technologic 246
8.55 Facebook 247
8.56 FICO 249
8.57 Fractal Analytics 250
8.58 Fujitsu 251
8.59 Fusion-io 253
8.60 GE (General Electric) 254
8.61 GoodData Corporation 255
8.62 Google 256
8.63 Guavus 257
8.64 HDS (Hitachi Data Systems) 258
8.65 Hortonworks 259
8.66 HP 260
8.67 IBM 261
8.68 iDashboards 262
8.69 Incorta 263
8.70 InetSoft Technology Corporation 264
8.71 InfiniDB 265
8.72 Infor 267
8.73 Informatica Corporation 268
8.74 Information Builders 269
8.75 Intel 270
8.76 Jedox 271
8.77 Jinfonet Software 272
8.78 Juniper Networks 273
8.79 Knime 274
8.80 Kofax 275
8.81 Kognitio 276
8.82 L-3 Communications 277
8.83 Lavastorm Analytics 278
8.84 Logi Analytics 279
8.85 Looker Data Sciences 280
8.86 LucidWorks 281
8.87 Manthan Software Services 282
8.88 MapR 283
8.89 MarkLogic 284
8.90 MemSQL 285
8.91 Microsoft 286
8.92 MicroStrategy 287
8.93 MongoDB (formerly 10gen) 288
8.94 Mu Sigma 289
8.95 NTT Data 290
8.96 Neo Technology 291
8.97 NetApp 292
8.98 OpenText Corporation 293
8.99 Opera Solutions 294
8.100 Oracle 295
8.101 Palantir Technologies 296
8.102 Panorama Software 297
8.103 ParStream 298
8.104 Pentaho 299
8.105 Phocas 300
8.106 Pivotal Software 301
8.107 Platfora 302
8.108 Prognoz 303
8.109 PwC 304
8.110 Pyramid Analytics 305
8.111 Qlik 306
8.112 Quantum Corporation 307
8.113 Qubole 308
8.114 Rackspace 309
8.115 RainStor 310
8.116 RapidMiner 311
8.117 Recorded Future 312
8.118 Revolution Analytics 313
8.119 RJMetrics 314
8.120 Salesforce.com 315
8.121 Sailthru 316
8.122 Salient Management Company 317
8.123 SAP 318
8.124 SAS Institute 319
8.125 SGI 320
8.126 SiSense 321
8.127 Software AG 322
8.128 Splice Machine 323
8.129 Splunk 324
8.130 Sqrrl 325
8.131 Strategy Companion 326
8.132 Supermicro 327
8.133 SynerScope 328
8.134 Tableau Software 329
8.135 Talend 330
8.136 Targit 331
8.137 TCS (Tata Consultancy Services) 332
8.138 Teradata 333
8.139 Think Big Analytics 334
8.140 ThoughtSpot 335
8.141 TIBCO Software 336
8.142 Tidemark 337
8.143 VMware (EMC Subsidiary) 338
8.144 WiPro 339
8.145 Yellowfin International 340
8.146 Zettics 341
8.147 Zoomdata 342
8.148 Zucchetti 343

9 Chapter 9: Conclusion & Strategic Recommendations 344
9.1 Big Data Technology: Beyond Data Capture & Analytics 344
9.2 Transforming IT from a Cost Center to a Profit Center 344
9.3 Can Privacy Implications Hinder Success? 345
9.4 Will Regulation have a Negative Impact on Big Data Investments? 345
9.5 Battling Organization & Data Silos 346
9.6 Software vs. Hardware Investments 347
9.7 Vendor Share: Who Leads the Market? 348
9.8 Big Data Driving Wider IT Industry Investments 349
9.9 Assessing the Impact of IoT & M2M 350
9.10 Recommendations 351
9.10.1 Big Data Hardware, Software & Professional Services Providers 351
9.10.2 Enterprises 352

List of Figures

Figure 1: Big Data Industry Roadmap 73
Figure 2: The Big Data Value Chain 76
Figure 3: Reactive vs. Proactive Analytics 81
Figure 4: Global Big Data Revenue: 2015 - 2030 ($ Million) 97
Figure 5: Global Big Data Revenue by Submarket: 2015 - 2030 ($ Million) 98
Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2015 - 2030 ($ Million) 99
Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2015 - 2030 ($ Million) 100
Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2015 - 2030 ($ Million) 101
Figure 9: Global Big Data SQL Submarket Revenue: 2015 - 2030 ($ Million) 102
Figure 10: Global Big Data NoSQL Submarket Revenue: 2015 - 2030 ($ Million) 103
Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2015 - 2030 ($ Million) 104
Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2015 - 2030 ($ Million) 105
Figure 13: Global Big Data Professional Services Submarket Revenue: 2015 - 2030 ($ Million) 106
Figure 14: Global Big Data Revenue by Vertical Market: 2015 - 2030 ($ Million) 107
Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2015 - 2030 ($ Million) 108
Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2015 - 2030 ($ Million) 109
Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2015 - 2030 ($ Million) 110
Figure 18: Global Big Data Revenue in the Education Sector: 2015 - 2030 ($ Million) 111
Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2015 - 2030 ($ Million) 112
Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2015 - 2030 ($ Million) 113
Figure 21: Global Big Data Revenue in the Insurance Sector: 2015 - 2030 ($ Million) 114
Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2015 - 2030 ($ Million) 115
Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2015 - 2030 ($ Million) 116
Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2015 - 2030 ($ Million) 117
Figure 25: Global Big Data Revenue in the Public Services Sector: 2015 - 2030 ($ Million) 118
Figure 26: Global Big Data Revenue in the Retail & Hospitality Sector: 2015 - 2030 ($ Million) 119
Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2015 - 2030 ($ Million) 120
Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2015 - 2030 ($ Million) 121
Figure 29: Global Big Data Revenue in the Wholesale Trade Sector: 2015 - 2030 ($ Million) 122
Figure 30: Global Big Data Revenue in Other Vertical Sectors: 2015 - 2030 ($ Million) 123
Figure 31: Big Data Revenue by Region: 2015 - 2030 ($ Million) 124
Figure 32: Asia Pacific Big Data Revenue: 2015 - 2030 ($ Million) 125
Figure 33: Asia Pacific Big Data Revenue by Country: 2015 - 2030 ($ Million) 126
Figure 34: Australia Big Data Revenue: 2015 - 2030 ($ Million) 127
Figure 35: China Big Data Revenue: 2015 - 2030 ($ Million) 128
Figure 36: India Big Data Revenue: 2015 - 2030 ($ Million) 129
Figure 37: Indonesia Big Data Revenue: 2015 - 2030 ($ Million) 130
Figure 38: Japan Big Data Revenue: 2015 - 2030 ($ Million) 131
Figure 39: Malaysia Big Data Revenue: 2015 - 2030 ($ Million) 132
Figure 40: Pakistan Big Data Revenue: 2015 - 2030 ($ Million) 133
Figure 41: Philippines Big Data Revenue: 2015 - 2030 ($ Million) 134
Figure 42: Singapore Big Data Revenue: 2015 - 2030 ($ Million) 135
Figure 43: South Korea Big Data Revenue: 2015 - 2030 ($ Million) 136
Figure 44: Taiwan Big Data Revenue: 2015 - 2030 ($ Million) 137
Figure 45: Thailand Big Data Revenue: 2015 - 2030 ($ Million) 138
Figure 46: Big Data Revenue in the Rest of Asia Pacific: 2015 - 2030 ($ Million) 139
Figure 47: Eastern Europe Big Data Revenue: 2015 - 2030 ($ Million) 140
Figure 48: Eastern Europe Big Data Revenue by Country: 2015 - 2030 ($ Million) 141
Figure 49: Czech Republic Big Data Revenue: 2015 - 2030 ($ Million) 142
Figure 50: Poland Big Data Revenue: 2015 - 2030 ($ Million) 143
Figure 51: Russia Big Data Revenue: 2015 - 2030 ($ Million) 144
Figure 52: Big Data Revenue in the Rest of Eastern Europe: 2015 - 2030 ($ Million) 145
Figure 53: Latin & Central America Big Data Revenue: 2015 - 2030 ($ Million) 146
Figure 54: Latin & Central America Big Data Revenue by Country: 2015 - 2030 ($ Million) 147
Figure 55: Argentina Big Data Revenue: 2015 - 2030 ($ Million) 148
Figure 56: Brazil Big Data Revenue: 2015 - 2030 ($ Million) 149
Figure 57: Mexico Big Data Revenue: 2015 - 2030 ($ Million) 150
Figure 58: Big Data Revenue in the Rest of Latin & Central America: 2015 - 2030 ($ Million) 151
Figure 59: Middle East & Africa Big Data Revenue: 2015 - 2030 ($ Million) 152
Figure 60: Middle East & Africa Big Data Revenue by Country: 2015 - 2030 ($ Million) 153
Figure 61: Israel Big Data Revenue: 2015 - 2030 ($ Million) 154
Figure 62: Qatar Big Data Revenue: 2015 - 2030 ($ Million) 155
Figure 63: Saudi Arabia Big Data Revenue: 2015 - 2030 ($ Million) 156
Figure 64: South Africa Big Data Revenue: 2015 - 2030 ($ Million) 157
Figure 65: UAE Big Data Revenue: 2015 - 2030 ($ Million) 158
Figure 66: Big Data Revenue in the Rest of the Middle East & Africa: 2015 - 2030 ($ Million) 159
Figure 67: North America Big Data Revenue: 2015 - 2030 ($ Million) 160
Figure 68: North America Big Data Revenue by Country: 2015 - 2030 ($ Million) 161
Figure 69: Canada Big Data Revenue: 2015 - 2030 ($ Million) 162
Figure 70: USA Big Data Revenue: 2015 - 2030 ($ Million) 163
Figure 71: Western Europe Big Data Revenue: 2015 - 2030 ($ Million) 164
Figure 72: Western Europe Big Data Revenue by Country: 2015 - 2030 ($ Million) 165
Figure 73: Denmark Big Data Revenue: 2015 - 2030 ($ Million) 166
Figure 74: Finland Big Data Revenue: 2015 - 2030 ($ Million) 167
Figure 75: France Big Data Revenue: 2015 - 2030 ($ Million) 168
Figure 76: Germany Big Data Revenue: 2015 - 2030 ($ Million) 169
Figure 77: Italy Big Data Revenue: 2015 - 2030 ($ Million) 170
Figure 78: Netherlands Big Data Revenue: 2015 - 2030 ($ Million) 171
Figure 79: Norway Big Data Revenue: 2015 - 2030 ($ Million) 172
Figure 80: Spain Big Data Revenue: 2015 - 2030 ($ Million) 173
Figure 81: Sweden Big Data Revenue: 2015 - 2030 ($ Million) 174
Figure 82: UK Big Data Revenue: 2015 - 2030 ($ Million) 175
Figure 83: Big Data Revenue in the Rest of Western Europe: 2015 - 2030 ($ Million) 176
Figure 84: Global Big Data Revenue by Hardware, Software & Professional Services ($ Million): 2015 - 2030 347
Figure 85: Big Data Vendor Market Share (%) 348
Figure 86: Global IT Expenditure Driven by Big Data Investments: 2015 - 2030 ($ Million) 349
Figure 87: Global M2M Connections by Access Technology (Millions): 2015 - 2030 350

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