The Big Data Market: 2017 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts


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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 over $57 Billion in 2017 alone. These investments are further expected to grow at a CAGR of approximately 10% over the next three years.

The “Big Data Market: 2017 – 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 2017 through to 2030. The forecasts are further segmented for 8 horizontal submarkets, 14 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 14 vertical markets
  • Big Data analytics technology and case studies
  • Big Data vendor market share
  • Company profiles and strategies of 240 Big Data ecosystem players
  • Strategic recommendations for Big Data hardware, software and professional services vendors, and enterprises
  • Market analysis and forecasts from 2017 till 2030

Forecast Segmentation

Market forecasts 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, Wholesale & Hospitality
  • Telecommunications
  • Utilities & Energy
  • 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?

The report has the following key findings:

  • In 2017, Big Data vendors will pocket over $57 Billion from hardware, software and professional services revenues. These investments are further expected to grow at a CAGR of approximately 10% over the next four years, eventually accounting for over $76 Billion by the end of 2020.
  • As part of wider plans to revitalize their economies, countries across the world are incorporating legislative initiatives to capitalize on Big Data. For example, the Japanese government is engaged in developing intellectual property protection and dispute resolution frameworks for Big Data assets, in a bid to encourage data sharing and accelerate the development of domestic industries.
  • By the end of 2017, SNS Research estimates that as much as 30% of all Big Data workloads will be processed via cloud services as enterprises seek to avoid large-scale infrastructure investments and security issues associated with on-premise implementations.
  • The vendor arena is continuing to consolidate with several prominent M&A deals such as computer hardware giant Dell's $60 Billion merger with data storage specialist EMC.

List of Companies Mentioned

  • 1010data
  • Absolutdata
  • Accenture
  • Actian Corporation
  • Actuate Corporation
  • Adaptive Insights
  • Adobe Systems
  • Advizor Solutions
  • AeroSpike
  • AFS Technologies
  • Airbus Group
  • Alameda County Social Services Agency
  • Alation
  • Algorithmia
  • Alluxio
  • Alphabet
  • Alpine Data
  • Alteryx
  • Altiscale
  • Ambulance Victoria
  • AMD (Advanced Micro Devices)
  • Amgen
  • Amino
  • ANSI (American National Standards Institute)
  • Antivia
  • Apixio
  • Arcadia Data
  • Arimo
  • ARM
  • ASF (Apache Software Foundation)
  • AstraZeneca
  • AT&T
  • Atos
  • AtScale
  • Attivio
  • Attunity
  • Automated Insights
  • AWS (Amazon Web Services)
  • Axiomatics
  • Ayasdi
  • BAE Systems
  • Baidu
  • Barclays Bank
  • Basho Technologies
  • BCG (Boston Consulting Group)
  • Bedrock Data
  • BetterWorks
  • Big Cloud Analytics
  • Big Panda
  • Bina Technologies
  • Biogen
  • Birst
  • Bitam
  • Blue Medora
  • BlueData Software
  • BlueTalon
  • BMC Software
  • BMW
  • BOARD International
  • Boeing
  • Booz Allen Hamilton
  • Boxever
  • British Gas
  • Brocade
  • BT Group
  • CACI International
  • Cambridge Semantics
  • Capgemini
  • Capital One Financial Corporation
  • Cazena
  • CBA (Commonwealth Bank of Australia)
  • CBP (U.S. Customs and Border Protection)
  • Centrifuge Systems
  • CenturyLink
  • Chartio
  • Cisco Systems
  • Civis Analytics
  • ClearStory Data
  • Cloudability
  • Cloudera
  • Clustrix
  • CognitiveScale
  • Collibra
  • Concurrent Computer Corporation
  • Confluent
  • Constant Contact
  • Contexti
  • Continuum Analytics
  • Coriant
  • Couchbase
  • Credit Agricole Group
  • CrowdFlower
  • CSA (Cloud Security Alliance)
  • CSCC (Cloud Standards Customer Council)
  • Databricks
  • DataGravity
  • Dataiku
  • Datameer
  • DataRobot
  • DataScience
  • DataStax
  • DataTorrent
  • Datawatch Corporation
  • Datos IO
  • DDN (DataDirect Networks)
  • Decisyon
  • Dell EMC
  • Dell Technologies
  • Deloitte
  • Delphi Automotive
  • Demandbase
  • Denodo Technologies
  • Denso Corporation
  • DGSE (General Directorate for External Security, France)
  • DHS (U.S. Department of Homeland Security)
  • Digital Reasoning Systems
  • Dimensional Insight
  • DMG  (Data Mining Group)
  • Dolphin Enterprise Solutions Corporation
  • Domino Data Lab
  • Domo
  • Dow Chemical Company
  • DriveScale
  • DT (Deutsche Telekom)
  • Dubai Police
  • Dundas Data Visualization
  • DXC Technology
  • eBay
  • Edith Cowen University
  • Eligotech
  • Engineering Group (Engineering Ingegneria Informatica)
  • EnterpriseDB
  • eQ Technologic
  • Ericsson
  • EXASOL
  • Facebook
  • FDNY (Fire Department of the City of New York)
  • FICO (Fair Isaac Corporation)
  • Ford Motor Company
  • Fractal Analytics
  • Fujitsu
  • Fuzzy Logix
  • Gainsight
  • GE (General Electric)
  • Glasgow City Council
  • Glassbeam
  • GoodData Corporation
  • Google
  • Greenwave Systems
  • GridGain Systems
  • GSK (GlaxoSmithKline)
  • Guavus
  • HDS (Hitachi Data Systems)
  • Hedvig
  • Hitachi
  • Hortonworks
  • HPE (Hewlett Packard Enterprise)
  • HSBC Group
  • Huawei
  • IBM Corporation
  • ICE (U.S. Immigration and Customs Enforcement)
  • iDashboards
  • IEC (International Electrotechnical Commission)
  • IEEE (Institute of Electrical and Electronics Engineers)
  • Impetus Technologies
  • INCITS (InterNational Committee for Information Technology Standards)
  • Incorta
  • InetSoft Technology Corporation
  • Infer
  • Infor
  • Informatica Corporation
  • Information Builders
  • Infosys
  • Infoworks
  • InsightSquared
  • Intel Corporation
  • Interana
  • InterSystems Corporation
  • ISO (International Organization for Standardization)
  • ITU (International Telecommunications Union)
  • Jedox
  • Jethro
  • Jinfonet Software
  • JJ Food Service
  • JPMorgan Chase & Co.
  • Juniper Networks
  • Kaiser Permanente
  • KALEAO
  • Keen IO
  • Kinetica
  • KNIME
  • Kofax
  • Kognitio
  • Kyvos Insights
  • Lavastorm
  • Lexalytics
  • Lexmark International
  • Linux Foundation
  • Logi Analytics
  • Longview Solutions
  • Looker Data Sciences
  • LucidWorks
  • Luminoso Technologies
  • Maana
  • Magento Commerce
  • Manthan Software Services
  • MapD Technologies
  • MapR Technologies
  • MariaDB Corporation
  • MarkLogic Corporation
  • Marriott International
  • Mathworks
  • Memphis Police Department
  • MemSQL
  • Mercer
  • METI (Ministry of Economy, Trade and Industry, Japan)
  • Metric Insights
  • Michelin
  • Microsoft Corporation
  • MicroStrategy
  • Ministry of State Security, China
  • Minitab
  • MongoDB
  • Mu Sigma
  • NASA (U.S. National Aeronautics and Space Administration)
  • Neo Technology
  • NetApp
  • Netflix
  • Neustar
  • New York State Department of Taxation and Finance
  • NextBio
  • NFL (U.S. National Football League)
  • Nimbix
  • NIST (U.S. National Institute of Standards and Technology)
  • Nokia
  • Northwest Analytics
  • Nottingham Trent University
  • Novartis
  • NSA (U.S. National Security Agency)
  • NTT Data Corporation
  • NTT Group
  • Numerify
  • NuoDB
  • Nutonian
  • NVIDIA Corporation
  • OASIS (Organization for the Advancement of Structured Information Standards)
  • Oblong Industries
  • ODaF (Open Data Foundation)
  • ODCA (Open Data Center Alliance)
  • ODPi (Open Ecosystem of Big Data)
  • Ofcom
  • OGC (Open Geospatial Consortium)
  • Oncor Electric Delivery Company
  • ONS (Office for National Statistics, United Kingdom)
  • OpenText Corporation
  • Opera Solutions
  • Optimal Plus
  • Oracle Corporation
  • Otonomo
  • OTP Bank
  • OVG Real Estate
  • Palantir Technologies
  • Panorama Software
  • Paxata
  • Pentaho Corporation
  • Pepperdata
  • Pfizer
  • Philips
  • Phocas Software
  • Pivotal Software
  • Predixion Software
  • Primerica
  • Procter & Gamble
  • Prognoz
  • Progress Software Corporation
  • Purdue University
  • PwC (PricewaterhouseCoopers International)
  • Pyramid Analytics
  • Qlik
  • Qualcomm
  • Quantum Corporation
  • Qubole
  • Rackspace
  • Radius Intelligence
  • RapidMiner
  • Recorded Future
  • Red Hat
  • Redis Labs
  • RedPoint Global
  • Reltio
  • Roche
  • Rosenberger
  • Royal Bank of Canada
  • Royal Dutch Shell
  • Royal Navy
  • RSA Group
  • RStudio
  • Ryft Systems
  • Sailthru
  • Salient Management Company
  • Samsung Electronics
  • Samsung Group
  • Samsung SDS
  • Sanofi
  • SAP
  • SAS Institute
  • ScaleDB
  • ScaleOut Software
  • Schneider Electric
  • SCIO Health Analytics
  • Seagate Technology
  • Siemens
  • Sinequa
  • SiSense
  • SnapLogic
  • Snowflake Computing
  • SoftBank Group
  • Software AG
  • SpagoBI Labs
  • Splice Machine
  • Splunk
  • Sqrrl
  • Strategy Companion Corporation
  • StreamSets
  • Striim
  • Sumo Logic
  • Supermicro (Super Micro Computer)
  • Syncsort
  • SynerScope
  • Tableau Software
  • Talena
  • Talend
  • Tamr
  • TARGIT
  • TCS (Tata Consultancy Services)
  • TEOCO
  • Teradata Corporation
  • Tesco
  • The Weather Company
  • Thomson Reuters
  • ThoughtSpot
  • TIBCO Software
  • Tidemark
  • TM Forum
  • T-Mobile USA
  • Toshiba Corporation
  • Toyota Motor Corporation
  • TPC (Transaction Processing Performance Council)
  • Trifacta
  • UCB
  • Unravel Data
  • USCIS (U.S. Citizenship and Immigration Services)
  • Valens
  • Verizon Communications
  • VMware
  • Vodafone Group
  • VoltDB
  • W3C (World Wide Web Consortium)
  • Walt Disney Company
  • Waterline Data
  • Wavefront
  • Western Digital Corporation
  • WiPro
  • Workday
  • Xevo
  • Xplenty
  • Yellowfin International
  • Yseop
  • Zendesk
  • Zoomdata
  • Zucchetti
  • Zurich Insurance Group

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

Table of Contents

1 Chapter 1: Introduction 25
1.1 Executive Summary 25
1.2 Topics Covered 27
1.3 Forecast Segmentation 28
1.4 Key Questions Answered 30
1.5 Key Findings 31
1.6 Methodology 32
1.7 Target Audience 33
1.8 Companies & Organizations Mentioned 34

2 Chapter 2: An Overview of Big Data 39
2.1 What is Big Data? 39
2.2 Key Approaches to Big Data Processing 39
2.2.1 Hadoop 40
2.2.2 NoSQL 42
2.2.3 MPAD (Massively Parallel Analytic Databases) 42
2.2.4 In-Memory Processing 43
2.2.5 Stream Processing Technologies 43
2.2.6 Spark 44
2.2.7 Other Databases & Analytic Technologies 44
2.3 Key Characteristics of Big Data 45
2.3.1 Volume 45
2.3.2 Velocity 45
2.3.3 Variety 45
2.3.4 Value 46
2.4 Market Growth Drivers 47
2.4.1 Awareness of Benefits 47
2.4.2 Maturation of Big Data Platforms 47
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 48
2.4.4 Growth of Data Volume, Velocity & Variety 48
2.4.5 Vendor Commitments & Partnerships 48
2.4.6 Technology Trends Lowering Entry Barriers 49
2.5 Market Barriers 49
2.5.1 Lack of Analytic Specialists 49
2.5.2 Uncertain Big Data Strategies 49
2.5.3 Organizational Resistance to Big Data Adoption 50
2.5.4 Technical Challenges: Scalability & Maintenance 50
2.5.5 Security & Privacy Concerns 50

3 Chapter 3: Big Data Analytics 52
3.1 What are Big Data Analytics? 52
3.2 The Importance of Analytics 52
3.3 Reactive vs. Proactive Analytics 53
3.4 Customer vs. Operational Analytics 54
3.5 Technology & Implementation Approaches 54
3.5.1 Grid Computing 54
3.5.2 In-Database Processing 55
3.5.3 In-Memory Analytics 55
3.5.4 Machine Learning & Data Mining 55
3.5.5 Predictive Analytics 56
3.5.6 NLP (Natural Language Processing) 56
3.5.7 Text Analytics 57
3.5.8 Visual Analytics 58
3.5.9 Social Media, IT & Telco Network Analytics 58

4 Chapter 4: Big Data in Automotive, Aerospace & Transportation 59
4.1 Overview & Investment Potential 59
4.2 Key Applications 59
4.2.1 Autonomous Driving 59
4.2.2 Warranty Analytics for Automotive OEMs 60
4.2.3 Predictive Aircraft Maintenance & Fuel Optimization 60
4.2.4 Air Traffic Control 61
4.2.5 Transport Fleet Optimization 61
4.2.6 UBI (Usage Based Insurance) 62
4.3 Case Studies 62
4.3.1 Delphi Automotive: Monetizing Connected Vehicles with Big Data 62
4.3.2 Boeing: Making Flying More Efficient with Big Data 64
4.3.3 BMW: Eliminating Defects in New Vehicle Models with Big Data 65
4.3.4 Toyota Motor Corporation: Powering Smart Cars with Big Data 66
4.3.5 Ford Motor Company: Making Efficient Transportation Decisions with Big Data 67

5 Chapter 5: Big Data in Banking & Securities 68
5.1 Overview & Investment Potential 68
5.2 Key Applications 68
5.2.1 Customer Retention & Personalized Product Offering 68
5.2.2 Risk Management 69
5.2.3 Fraud Detection 69
5.2.4 Credit Scoring 69
5.3 Case Studies 69
5.3.1 HSBC Group: Avoiding Regulatory Penalties with Big Data 70
5.3.2 JPMorgan Chase & Co.: Improving Business Processes with Big Data 71
5.3.3 OTP Bank: Reducing Loan Defaults with Big Data 72
5.3.4 CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data 73

6 Chapter 6: Big Data in Defense & Intelligence 74
6.1 Overview & Investment Potential 74
6.2 Key Applications 74
6.2.1 Intelligence Gathering 74
6.2.2 Battlefield Analytics 75
6.2.3 Energy Saving Opportunities in the Battlefield 75
6.2.4 Preventing Injuries on the Battlefield 76
6.3 Case Studies 77
6.3.1 U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data 77
6.3.2 Royal Navy: Empowering Submarine Warfare with Big Data 78
6.3.3 NSA (National Security Agency): Capitalizing on Big Data to Detect Threats 79
6.3.4 Ministry of State Security, China: Predictive Policing with Big Data 80
6.3.5 French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data 81

7 Chapter 7: Big Data in Education 82
7.1 Overview & Investment Potential 82
7.2 Key Applications 82
7.2.1 Information Integration 82
7.2.2 Identifying Learning Patterns 83
7.2.3 Enabling Student-Directed Learning 83
7.3 Case Studies 83
7.3.1 Purdue University: Ensuring Successful Higher Education Outcomes with Big Data 84
7.3.2 Nottingham Trent University: Successful Student Outcomes with Big Data 85
7.3.3 Edith Cowen University: Increasing Student Retention with Big Data 86

8 Chapter 8: Big Data in Healthcare & Pharma 87
8.1 Overview & Investment Potential 87
8.2 Key Applications 87
8.2.1 Managing Population Health Efficiently 87
8.2.2 Improving Patient Care with Medical Data Analytics 88
8.2.3 Improving Clinical Development & Trials 88
8.2.4 Drug Development: Improving Time to Market 88
8.3 Case Studies 89
8.3.1 Amino: Healthcare Transparency with Big Data 89
8.3.2 Novartis: Digitizing Healthcare with Big Data 90
8.3.3 GSK (GlaxoSmithKline): Accelerating Drug Discovering with Big Data 91
8.3.4 Pfizer: Developing Effective and Targeted Therapies with Big Data 92
8.3.5 Roche: Personalizing Healthcare with Big Data 93
8.3.6 Sanofi: Proactive Diabetes Care with Big Data 94

9 Chapter 9: Big Data in Smart Cities & Intelligent Buildings 96
9.1 Overview & Investment Potential 96
9.2 Key Applications 96
9.2.1 Energy Optimization & Fault Detection 97
9.2.2 Intelligent Building Analytics 97
9.2.3 Urban Transportation Management 97
9.2.4 Optimizing Energy Production 98
9.2.5 Water Management 98
9.2.6 Urban Waste Management 98
9.3 Case Studies 98
9.3.1 Singapore: Building a Smart Nation with Big Data 99
9.3.2 Glasgow City Council: Promoting Smart City Efforts with Big Data 100
9.3.3 OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data 101

10 Chapter 10: Big Data in Insurance 102
10.1 Overview & Investment Potential 102
10.2 Key Applications 102
10.2.1 Claims Fraud Mitigation 102
10.2.2 Customer Retention & Profiling 103
10.2.3 Risk Management 103
10.3 Case Studies 103
10.3.1 Zurich Insurance Group: Enhancing Risk Management with Big Data 103
10.3.2 RSA Group: Improving Customer Relations with Big Data 105
10.3.3 Primerica: Improving Insurance Sales Force Productivity with Big Data 106

11 Chapter 11: Big Data in Manufacturing & Natural Resources 107
11.1 Overview & Investment Potential 107
11.2 Key Applications 107
11.2.1 Asset Maintenance & Downtime Reduction 107
11.2.2 Quality & Environmental Impact Control 108
11.2.3 Optimized Supply Chain 108
11.2.4 Exploration & Identification of Natural Resources 108
11.3 Case Studies 109
11.3.1 Intel Corporation: Cutting Manufacturing Costs with Big Data 109
11.3.2 Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data 110
11.3.3 Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data 111
11.3.4 Brunei: Saving Natural Resources with Big Data 112

12 Chapter 12: Big Data in Web, Media & Entertainment 113
12.1 Overview & Investment Potential 113
12.2 Key Applications 113
12.2.1 Audience & Advertising Optimization 114
12.2.2 Channel Optimization 114
12.2.3 Recommendation Engines 114
12.2.4 Optimized Search 114
12.2.5 Live Sports Event Analytics 115
12.2.6 Outsourcing Big Data Analytics to Other Verticals 115
12.3 Case Studies 115
12.3.1 Netflix: Improving Viewership with Big Data 115
12.3.2 NFL (National Football League): Improving Stadium Experience with Big Data 117
12.3.3 Walt Disney Company: Enhancing Theme Park Experience with Big Data 118
12.3.4 Baidu: Reshaping Search Capabilities with Big Data 119
12.3.5 Constant Contact: Effective Marketing with Big Data 120

13 Chapter 13: Big Data in Public Safety & Homeland Security 121
13.1 Overview & Investment Potential 121
13.2 Key Applications 121
13.2.1 Cyber Crime Mitigation 122
13.2.2 Crime Prediction Analytics 122
13.2.3 Video Analytics & Situational Awareness 122
13.3 Case Studies 123
13.3.1 DHS (U.S. Department of Homeland Security): Identifying Threats to Physical and Network Infrastructure with Big Data 123
13.3.2 Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data 124
13.3.3 Memphis Police Department: Crime Reduction with Big Data 125

14 Chapter 14: Big Data in Public Services 126
14.1 Overview & Investment Potential 126
14.2 Key Applications 126
14.2.1 Public Sentiment Analysis 126
14.2.2 Tax Collection & Fraud Detection 127
14.2.3 Economic Analysis 127
14.2.4 Predicting & Mitigating Disasters 127
14.3 Case Studies 128
14.3.1 ONS (Office for National Statistics): Exploring the UK Economy with Big Data 128
14.3.2 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data 129
14.3.3 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data 130
14.3.4 City of Chicago: Improving Government Productivity with Big Data 131
14.3.5 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data 132
14.3.6 Ambulance Victoria: Improving Patient Survival Rates with Big Data 133

15 Chapter 15: Big Data in Retail, Wholesale & Hospitality 134
15.1 Overview & Investment Potential 134
15.2 Key Applications 134
15.2.1 Customer Sentiment Analysis 135
15.2.2 Customer & Branch Segmentation 135
15.2.3 Price Optimization 135
15.2.4 Personalized Marketing 135
15.2.5 Optimizing & Monitoring the Supply Chain 136
15.2.6 In-Field Sales Analytics 136
15.3 Case Studies 136
15.3.1 Walmart: Making Smarter Stocking Decision with Big Data 137
15.3.2 Tesco: Reducing Supermarket Energy Bills with Big Data 138
15.3.3 Marriott International: Elevating Guest Services with Big Data 139
15.3.4 JJ Food Service: Predictive Wholesale Shopping Lists with Big Data 140

16 Chapter 16: Big Data in Telecommunications 141
16.1 Overview & Investment Potential 141
16.2 Key Applications 141
16.2.1 Network Performance & Coverage Optimization 141
16.2.2 Customer Churn Prevention 142
16.2.3 Personalized Marketing 142
16.2.4 Tailored Location Based Services 142
16.2.5 Fraud Detection 142
16.3 Case Studies 143
16.3.1 BT Group: Hunting Down Nuisance Callers with Big Data 143
16.3.2 AT&T: Smart Network Management with Big Data 144
16.3.3 T-Mobile USA: Cutting Down Churn Rate with Big Data 145
16.3.4 TEOCO: Helping Service Providers Save Millions with Big Data 146
16.3.5 Freedom Mobile: Optimizing Video Quality with Big Data 147
16.3.6 Coriant: SaaS Based Analytics with Big Data 148

17 Chapter 17: Big Data in Utilities & Energy 149
17.1 Overview & Investment Potential 149
17.2 Key Applications 149
17.2.1 Customer Retention 149
17.2.2 Forecasting Energy 150
17.2.3 Billing Analytics 150
17.2.4 Predictive Maintenance 150
17.2.5 Maximizing the Potential of Drilling 150
17.2.6 Production Optimization 151
17.3 Case Studies 151
17.3.1 Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data 151
17.3.2 British Gas: Improving Customer Service with Big Data 152
17.3.3 Oncor Electric Delivery: Intelligent Power Grid Management with Big Data 153

18 Chapter 18: Big Data Industry Roadmap & Value Chain 154
18.1 Big Data Industry Roadmap 154
18.1.1 2017 – 2020: Investments in Predictive Analytics & SaaS-Based Big Data Offerings 154
18.1.2 2020 – 2025: Growing Focus on Cognitive & Personalized Analytics 155
18.1.3 2025 – 2030: Convergence with Future IoT Applications 155
18.2 The Big Data Value Chain 156
18.2.1 Hardware Providers 156
18.2.1.1 Storage & Compute Infrastructure Providers 156
18.2.1.2 Networking Infrastructure Providers 157
18.2.2 Software Providers 158
18.2.2.1 Hadoop & Infrastructure Software Providers 158
18.2.2.2 SQL & NoSQL Providers 158
18.2.2.3 Analytic Platform & Application Software Providers 158
18.2.2.4 Cloud Platform Providers 159
18.2.3 Professional Services Providers 159
18.2.4 End-to-End Solution Providers 159
18.2.5 Vertical Enterprises 159

19 Chapter 19: Standardization & Regulatory Initiatives 160
19.1 ASF (Apache Software Foundation) 160
19.1.1 Management of Hadoop 160
19.1.2 Big Data Projects Beyond Hadoop 160
19.2 CSA (Cloud Security Alliance) 163
19.2.1 BDWG (Big Data Working Group) 163
19.3 CSCC (Cloud Standards Customer Council) 164
19.3.1 Big Data Working Group 164
19.4 DMG  (Data Mining Group) 165
19.4.1 PMML (Predictive Model Markup Language) Working Group 165
19.4.2 PFA (Portable Format for Analytics) Working Group 165
19.5 IEEE (Institute of Electrical and Electronics Engineers) –Big Data Initiative 166
19.6 INCITS (InterNational Committee for Information Technology Standards) 167
19.6.1 Big Data Technical Committee 167
19.7 ISO (International Organization for Standardization) 168
19.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 168
19.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 169
19.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 169
19.7.4 ISO/IEC JTC 1/WG 9: Big Data 169
19.7.5 Collaborations with Other ISO Work Groups 171
19.8 ITU (International Telecommunications Union) 171
19.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities 171
19.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 172
19.8.3 Other Relevant Work 173
19.9 Linux Foundation 173
19.9.1 ODPi (Open Ecosystem of Big Data) 173
19.10 NIST (National Institute of Standards and Technology) 174
19.10.1 NBD-PWG (NIST Big Data Public Working Group) 174
19.11 OASIS (Organization for the Advancement of Structured Information Standards) 175
19.11.1 Technical Committees 175
19.12 ODaF (Open Data Foundation) 176
19.12.1 Big Data Accessibility 176
19.13 ODCA (Open Data Center Alliance) 176
19.13.1 Work on Big Data 176
19.14 OGC (Open Geospatial Consortium) 177
19.14.1 Big Data DWG (Domain Working Group) 177
19.15 TM Forum 177
19.15.1 Big Data Analytics Strategic Program 177
19.16 TPC (Transaction Processing Performance Council) 178
19.16.1 TPC-BDWG (TPC Big Data Working Group) 178
19.17 W3C (World Wide Web Consortium) 178
19.17.1 Big Data Community Group 178
19.17.2 Open Government Community Group 179

20 Chapter 20: Market Analysis & Forecasts 180
20.1 Global Outlook for the Big Data Market 180
20.2 Submarket Segmentation 181
20.2.1 Storage and Compute Infrastructure 182
20.2.2 Networking Infrastructure 183
20.2.3 Hadoop & Infrastructure Software 184
20.2.4 SQL 185
20.2.5 NoSQL 186
20.2.6 Analytic Platforms & Applications 187
20.2.7 Cloud Platforms 188
20.2.8 Professional Services 189
20.3 Vertical Market Segmentation 190
20.3.1 Automotive, Aerospace & Transportation 191
20.3.2 Banking & Securities 192
20.3.3 Defense & Intelligence 193
20.3.4 Education 194
20.3.5 Healthcare & Pharmaceutical 195
20.3.6 Smart Cities & Intelligent Buildings 196
20.3.7 Insurance 197
20.3.8 Manufacturing & Natural Resources 198
20.3.9 Media & Entertainment 199
20.3.10 Public Safety & Homeland Security 200
20.3.11 Public Services 201
20.3.12 Retail, Wholesale & Hospitality 202
20.3.13 Telecommunications 203
20.3.14 Utilities & Energy 204
20.3.15 Other Sectors 205
20.4 Regional Outlook 206
20.5 Asia Pacific 207
20.5.1 Country Level Segmentation 207
20.5.2 Australia 208
20.5.3 China 208
20.5.4 India 209
20.5.5 Indonesia 209
20.5.6 Japan 210
20.5.7 Malaysia 210
20.5.8 Pakistan 211
20.5.9 Philippines 211
20.5.10 Singapore 212
20.5.11 South Korea 212
20.5.12 Taiwan 213
20.5.13 Thailand 213
20.5.14 Rest of Asia Pacific 214
20.6 Eastern Europe 215
20.6.1 Country Level Segmentation 215
20.6.2 Czech Republic 216
20.6.3 Poland 216
20.6.4 Russia 217
20.6.5 Rest of Eastern Europe 217
20.7 Latin & Central America 218
20.7.1 Country Level Segmentation 218
20.7.2 Argentina 219
20.7.3 Brazil 219
20.7.4 Mexico 220
20.7.5 Rest of Latin & Central America 220
20.8 Middle East & Africa 221
20.8.1 Country Level Segmentation 221
20.8.2 Israel 222
20.8.3 Qatar 222
20.8.4 Saudi Arabia 223
20.8.5 South Africa 223
20.8.6 UAE 224
20.8.7 Rest of the Middle East & Africa 224
20.9 North America 225
20.9.1 Country Level Segmentation 225
20.9.2 Canada 226
20.9.3 USA 226
20.10 Western Europe 227
20.10.1 Country Level Segmentation 227
20.10.2 Denmark 228
20.10.3 Finland 228
20.10.4 France 229
20.10.5 Germany 229
20.10.6 Italy 230
20.10.7 Netherlands 230
20.10.8 Norway 231
20.10.9 Spain 231
20.10.10 Sweden 232
20.10.11 UK 232
20.10.12 Rest of Western Europe 233

21 Chapter 21: Vendor Landscape 234
21.1 1010data 234
21.2 Absolutdata 235
21.3 Accenture 236
21.4 Actian Corporation 237
21.5 Adaptive Insights 238
21.6 Advizor Solutions 239
21.7 AeroSpike 240
21.8 AFS Technologies 241
21.9 Alation 242
21.10 Algorithmia 243
21.11 Alluxio 244
21.12 Alpine Data 245
21.13 Alteryx 246
21.14 AMD (Advanced Micro Devices) 247
21.15 Apixio 248
21.16 Arcadia Data 249
21.17 Arimo 250
21.18 ARM 251
21.19 AtScale 252
21.20 Attivio 253
21.21 Attunity 254
21.22 Automated Insights 255
21.23 AWS (Amazon Web Services) 256
21.24 Axiomatics 257
21.25 Ayasdi 258
21.26 Basho Technologies 259
21.27 BCG (Boston Consulting Group) 260
21.28 Bedrock Data 261
21.29 BetterWorks 262
21.30 Big Cloud Analytics 263
21.31 Big Panda 264
21.32 Birst 265
21.33 Bitam 266
21.34 Blue Medora 267
21.35 BlueData Software 268
21.36 BlueTalon 269
21.37 BMC Software 270
21.38 BOARD International 271
21.39 Booz Allen Hamilton 272
21.40 Boxever 273
21.41 CACI International 274
21.42 Cambridge Semantics 275
21.43 Capgemini 276
21.44 Cazena 277
21.45 Centrifuge Systems 278
21.46 CenturyLink 279
21.47 Chartio 280
21.48 Cisco Systems 281
21.49 Civis Analytics 282
21.50 ClearStory Data 283
21.51 Cloudability 284
21.52 Cloudera 285
21.53 Clustrix 286
21.54 CognitiveScale 287
21.55 Collibra 288
21.56 Concurrent Computer Corporation 289
21.57 Confluent 290
21.58 Contexti 291
21.59 Continuum Analytics 292
21.60 Couchbase 293
21.61 CrowdFlower 294
21.62 Databricks 295
21.63 DataGravity 296
21.64 Dataiku 297
21.65 Datameer 298
21.66 DataRobot 299
21.67 DataScience 300
21.68 DataStax 301
21.69 DataTorrent 302
21.70 Datawatch Corporation 303
21.71 Datos IO 304
21.72 DDN (DataDirect Networks) 305
21.73 Decisyon 306
21.74 Dell Technologies 307
21.75 Deloitte 308
21.76 Demandbase 309
21.77 Denodo Technologies 310
21.78 Digital Reasoning Systems 311
21.79 Dimensional Insight 312
21.80 Dolphin Enterprise Solutions Corporation 313
21.81 Domino Data Lab 314
21.82 Domo 315
21.83 DriveScale 316
21.84 Dundas Data Visualization 317
21.85 DXC Technology 318
21.86 Eligotech 319
21.87 Engineering Group (Engineering Ingegneria Informatica) 320
21.88 EnterpriseDB 321
21.89 eQ Technologic 322
21.90 Ericsson 323
21.91 EXASOL 324
21.92 Facebook 325
21.93 FICO (Fair Isaac Corporation) 326
21.94 Fractal Analytics 327
21.95 Fujitsu 328
21.96 Fuzzy Logix 330
21.97 Gainsight 331
21.98 GE (General Electric) 332
21.99 Glassbeam 333
21.100 GoodData Corporation 334
21.101 Google 335
21.102 Greenwave Systems 336
21.103 GridGain Systems 337
21.104 Guavus 338
21.105 H2O.ai 339
21.106 HDS (Hitachi Data Systems) 340
21.107 Hedvig 341
21.108 Hortonworks 342
21.109 HPE (Hewlett Packard Enterprise) 343
21.110 Huawei 345
21.111 IBM Corporation 346
21.112 iDashboards 348
21.113 Impetus Technologies 349
21.114 Incorta 350
21.115 InetSoft Technology Corporation 351
21.116 Infer 352
21.117 Infor 353
21.118 Informatica Corporation 354
21.119 Information Builders 355
21.120 Infosys 356
21.121 Infoworks 357
21.122 Insightsoftware.com 358
21.123 InsightSquared 359
21.124 Intel Corporation 360
21.125 Interana 361
21.126 InterSystems Corporation 362
21.127 Jedox 363
21.128 Jethro 364
21.129 Jinfonet Software 365
21.130 Juniper Networks 366
21.131 KALEAO 367
21.132 Keen IO 368
21.133 Kinetica 369
21.134 KNIME 370
21.135 Kognitio 371
21.136 Kyvos Insights 372
21.137 Lavastorm 373
21.138 Lexalytics 374
21.139 Lexmark International 375
21.140 Logi Analytics 376
21.141 Longview Solutions 377
21.142 Looker Data Sciences 378
21.143 LucidWorks 379
21.144 Luminoso Technologies 380
21.145 Maana 381
21.146 Magento Commerce 382
21.147 Manthan Software Services 383
21.148 MapD Technologies 384
21.149 MapR Technologies 385
21.150 MariaDB Corporation 386
21.151 MarkLogic Corporation 387
21.152 Mathworks 388
21.153 MemSQL 389
21.154 Metric Insights 390
21.155 Microsoft Corporation 391
21.156 MicroStrategy 392
21.157 Minitab 393
21.158 MongoDB 394
21.159 Mu Sigma 395
21.160 Neo Technology 396
21.161 NetApp 397
21.162 Nimbix 398
21.163 Nokia 399
21.164 NTT Data Corporation 400
21.165 Numerify 401
21.166 NuoDB 402
21.167 Nutonian 403
21.168 NVIDIA Corporation 404
21.169 Oblong Industries 405
21.170 OpenText Corporation 406
21.171 Opera Solutions 408
21.172 Optimal Plus 409
21.173 Oracle Corporation 410
21.174 Palantir Technologies 412
21.175 Panorama Software 413
21.176 Paxata 414
21.177 Pentaho Corporation 415
21.178 Pepperdata 416
21.179 Phocas Software 417
21.180 Pivotal Software 418
21.181 Prognoz 420
21.182 Progress Software Corporation 421
21.183 PwC (PricewaterhouseCoopers International) 422
21.184 Pyramid Analytics 423
21.185 Qlik 424
21.186 Quantum Corporation 425
21.187 Qubole 426
21.188 Rackspace 427
21.189 Radius Intelligence 428
21.190 RapidMiner 429
21.191 Recorded Future 430
21.192 Red Hat 431
21.193 Redis Labs 432
21.194 RedPoint Global 433
21.195 Reltio 434
21.196 RStudio 435
21.197 Ryft Systems 436
21.198 Sailthru 437
21.199 Salesforce.com 438
21.200 Salient Management Company 439
21.201 Samsung Group 440
21.202 SAP 441
21.203 SAS Institute 442
21.204 ScaleDB 443
21.205 ScaleOut Software 444
21.206 SCIO Health Analytics 445
21.207 Seagate Technology 446
21.208 Sinequa 447
21.209 SiSense 448
21.210 SnapLogic 449
21.211 Snowflake Computing 450
21.212 Software AG 451
21.213 Splice Machine 452
21.214 Splunk 453
21.215 Sqrrl 454
21.216 Strategy Companion Corporation 455
21.217 StreamSets 456
21.218 Striim 457
21.219 Sumo Logic 458
21.220 Supermicro (Super Micro Computer) 459
21.221 Syncsort 460
21.222 SynerScope 461
21.223 Tableau Software 462
21.224 Talena 463
21.225 Talend 464
21.226 Tamr 465
21.227 TARGIT 466
21.228 TCS (Tata Consultancy Services) 467
21.229 Teradata Corporation 468
21.230 ThoughtSpot 470
21.231 TIBCO Software 471
21.232 Tidemark 472
21.233 Toshiba Corporation 473
21.234 Trifacta 474
21.235 Unravel Data 475
21.236 VMware 476
21.237 VoltDB 477
21.238 Waterline Data 478
21.239 Western Digital Corporation 479
21.240 WiPro 480
21.241 Workday 481
21.242 Xplenty 482
21.243 Yellowfin International 483
21.244 Yseop 484
21.245 Zendesk 485
21.246 Zoomdata 486
21.247 Zucchetti 487

22 Chapter 22: Conclusion & Strategic Recommendations 488
22.1 Big Data Technology: Beyond Data Capture & Analytics 488
22.2 Transforming IT from a Cost Center to a Profit Center 488
22.3 Can Privacy Implications Hinder Success? 489
22.4 Maximizing Innovation with Careful Regulation 489
22.5 Battling Organizational & Data Silos 490
22.6 Moving Big Data to the Cloud 491
22.7 Software vs. Hardware Investments 492
22.8 Vendor Share: Who Leads the Market? 493
22.9 Moving Towards Consolidation: Review of M&A Activity in the Vendor Arena 494
22.10 Big Data Driving Wider IT Industry Investments 495
22.11 Assessing the Impact of IoT & M2M 496
22.12 Recommendations 497
22.12.1 Big Data Hardware, Software & Professional Services Providers 497
22.12.2 Enterprises 498

Figure 1: Hadoop Architecture 41
Figure 2: Reactive vs. Proactive Analytics 54
Figure 3: Big Data Industry Roadmap 155
Figure 4: Big Data Value Chain 157
Figure 5: Key Aspects of Big Data Standardization 167
Figure 6: Global Big Data Revenue: 2017 - 2030 ($ Million) 181
Figure 7: Global Big Data Revenue by Submarket: 2017 - 2030 ($ Million) 182
Figure 8: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2017 - 2030 ($ Million) 183
Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2017 - 2030 ($ Million) 184
Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2017 - 2030 ($ Million) 185
Figure 11: Global Big Data SQL Submarket Revenue: 2017 - 2030 ($ Million) 186
Figure 12: Global Big Data NoSQL Submarket Revenue: 2017 - 2030 ($ Million) 187
Figure 13: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2017 - 2030 ($ Million) 188
Figure 14: Global Big Data Cloud Platforms Submarket Revenue: 2017 - 2030 ($ Million) 189
Figure 15: Global Big Data Professional Services Submarket Revenue: 2017 - 2030 ($ Million) 190
Figure 16: Global Big Data Revenue by Vertical Market: 2017 - 2030 ($ Million) 191
Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2017 - 2030 ($ Million) 192
Figure 18: Global Big Data Revenue in the Banking & Securities Sector: 2017 - 2030 ($ Million) 193
Figure 19: Global Big Data Revenue in the Defense & Intelligence Sector: 2017 - 2030 ($ Million) 194
Figure 20: Global Big Data Revenue in the Education Sector: 2017 - 2030 ($ Million) 195
Figure 21: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2017 - 2030 ($ Million) 196
Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2017 - 2030 ($ Million) 197
Figure 23: Global Big Data Revenue in the Insurance Sector: 2017 - 2030 ($ Million) 198
Figure 24: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2017 - 2030 ($ Million) 199
Figure 25: Global Big Data Revenue in the Media & Entertainment Sector: 2017 - 2030 ($ Million) 200
Figure 26: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2017 - 2030 ($ Million) 201
Figure 27: Global Big Data Revenue in the Public Services Sector: 2017 - 2030 ($ Million) 202
Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2017 - 2030 ($ Million) 203
Figure 29: Global Big Data Revenue in the Telecommunications Sector: 2017 - 2030 ($ Million) 204
Figure 30: Global Big Data Revenue in the Utilities & Energy Sector: 2017 - 2030 ($ Million) 205
Figure 31: Global Big Data Revenue in Other Vertical Sectors: 2017 - 2030 ($ Million) 206
Figure 32: Big Data Revenue by Region: 2017 - 2030 ($ Million) 207
Figure 33: Asia Pacific Big Data Revenue: 2017 - 2030 ($ Million) 208
Figure 34: Asia Pacific Big Data Revenue by Country: 2017 - 2030 ($ Million) 208
Figure 35: Australia Big Data Revenue: 2017 - 2030 ($ Million) 209
Figure 36: China Big Data Revenue: 2017 - 2030 ($ Million) 209
Figure 37: India Big Data Revenue: 2017 - 2030 ($ Million) 210
Figure 38: Indonesia Big Data Revenue: 2017 - 2030 ($ Million) 210
Figure 39: Japan Big Data Revenue: 2017 - 2030 ($ Million) 211
Figure 40: Malaysia Big Data Revenue: 2017 - 2030 ($ Million) 211
Figure 41: Pakistan Big Data Revenue: 2017 - 2030 ($ Million) 212
Figure 42: Philippines Big Data Revenue: 2017 - 2030 ($ Million) 212
Figure 43: Singapore Big Data Revenue: 2017 - 2030 ($ Million) 213
Figure 44: South Korea Big Data Revenue: 2017 - 2030 ($ Million) 213
Figure 45: Taiwan Big Data Revenue: 2017 - 2030 ($ Million) 214
Figure 46: Thailand Big Data Revenue: 2017 - 2030 ($ Million) 214
Figure 47: Big Data Revenue in the Rest of Asia Pacific: 2017 - 2030 ($ Million) 215
Figure 48: Eastern Europe Big Data Revenue: 2017 - 2030 ($ Million) 216
Figure 49: Eastern Europe Big Data Revenue by Country: 2017 - 2030 ($ Million) 216
Figure 50: Czech Republic Big Data Revenue: 2017 - 2030 ($ Million) 217
Figure 51: Poland Big Data Revenue: 2017 - 2030 ($ Million) 217
Figure 52: Russia Big Data Revenue: 2017 - 2030 ($ Million) 218
Figure 53: Big Data Revenue in the Rest of Eastern Europe: 2017 - 2030 ($ Million) 218
Figure 54: Latin & Central America Big Data Revenue: 2017 - 2030 ($ Million) 219
Figure 55: Latin & Central America Big Data Revenue by Country: 2017 - 2030 ($ Million) 219
Figure 56: Argentina Big Data Revenue: 2017 - 2030 ($ Million) 220
Figure 57: Brazil Big Data Revenue: 2017 - 2030 ($ Million) 220
Figure 58: Mexico Big Data Revenue: 2017 - 2030 ($ Million) 221
Figure 59: Big Data Revenue in the Rest of Latin & Central America: 2017 - 2030 ($ Million) 221
Figure 60: Middle East & Africa Big Data Revenue: 2017 - 2030 ($ Million) 222
Figure 61: Middle East & Africa Big Data Revenue by Country: 2017 - 2030 ($ Million) 222
Figure 62: Israel Big Data Revenue: 2017 - 2030 ($ Million) 223
Figure 63: Qatar Big Data Revenue: 2017 - 2030 ($ Million) 223
Figure 64: Saudi Arabia Big Data Revenue: 2017 - 2030 ($ Million) 224
Figure 65: South Africa Big Data Revenue: 2017 - 2030 ($ Million) 224
Figure 66: UAE Big Data Revenue: 2017 - 2030 ($ Million) 225
Figure 67: Big Data Revenue in the Rest of the Middle East & Africa: 2017 - 2030 ($ Million) 225
Figure 68: North America Big Data Revenue: 2017 - 2030 ($ Million) 226
Figure 69: North America Big Data Revenue by Country: 2017 - 2030 ($ Million) 226
Figure 70: Canada Big Data Revenue: 2017 - 2030 ($ Million) 227
Figure 71: USA Big Data Revenue: 2017 - 2030 ($ Million) 227
Figure 72: Western Europe Big Data Revenue: 2017 - 2030 ($ Million) 228
Figure 73: Western Europe Big Data Revenue by Country: 2017 - 2030 ($ Million) 228
Figure 74: Denmark Big Data Revenue: 2017 - 2030 ($ Million) 229
Figure 75: Finland Big Data Revenue: 2017 - 2030 ($ Million) 229
Figure 76: France Big Data Revenue: 2017 - 2030 ($ Million) 230
Figure 77: Germany Big Data Revenue: 2017 - 2030 ($ Million) 230
Figure 78: Italy Big Data Revenue: 2017 - 2030 ($ Million) 231
Figure 79: Netherlands Big Data Revenue: 2017 - 2030 ($ Million) 231
Figure 80: Norway Big Data Revenue: 2017 - 2030 ($ Million) 232
Figure 81: Spain Big Data Revenue: 2017 - 2030 ($ Million) 232
Figure 82: Sweden Big Data Revenue: 2017 - 2030 ($ Million) 233
Figure 83: UK Big Data Revenue: 2017 - 2030 ($ Million) 233
Figure 84: Big Data Revenue in the Rest of Western Europe: 2017 - 2030 ($ Million) 234
Figure 85: Global Big Data Workload Distribution by Environment: 2017 - 2030 (%) 492
Figure 86: Global Big Data Revenue by Hardware, Software & Professional Services: 2017 - 2030 ($ Million) 493
Figure 87: Big Data Vendor Market Share (%) 494
Figure 88: Global IT Expenditure Driven by Big Data Investments: 2017 - 2030 ($ Million) 496
Figure 89: Global M2M Connections by Access Technology: 2017 - 2030 (Millions) 497