The Big Data Market: 2016 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

Date: June 10, 2016
Pages: 390
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Report type: Strategic Report
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The Big Data Market: 2016 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals and Forecasts
“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 $46 Billion in 2016 alone. These investments are further expected to grow at a CAGR of 12% over the next four years.

The “Big Data Market: 2016 – 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 2016 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.
1 CHAPTER 1: INTRODUCTION

1.1 Executive Summary
1.2 Topics Covered
1.3 Historical Revenue & Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned

2 CHAPTER 2: AN OVERVIEW OF BIG DATA

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

3 CHAPTER 3: BIG DATA ANALYTICS

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

4 CHAPTER 4: BIG DATA IN AUTOMOTIVE, AEROSPACE & TRANSPORTATION

4.1 Overview & Investment Potential
4.2 Key Applications
  4.2.1 Warranty Analytics for Automotive OEMs
  4.2.2 Predictive Aircraft Maintenance & Fuel Optimization
  4.2.3 Air Traffic Control
  4.2.4 Transport Fleet Optimization
4.3 Case Studies
  4.3.1 Boeing: Making Flying More Efficient with Big Data
  4.3.2 BMW: Eliminating Defects in New Vehicle Models with Big Data
  4.3.3 Toyota Motor Corporation: Powering Smart Cars with Big Data
  4.3.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data

5 CHAPTER 5: BIG DATA IN BANKING & SECURITIES

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

6 CHAPTER 6: BIG DATA IN DEFENSE & INTELLIGENCE

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

7 CHAPTER 7: BIG DATA IN EDUCATION

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

8 CHAPTER 8: BIG DATA IN HEALTHCARE & PHARMA

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

9 CHAPTER 9: BIG DATA IN SMART CITIES & INTELLIGENT BUILDINGS

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

10 CHAPTER 10: BIG DATA IN INSURANCE

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

11 CHAPTER 11: BIG DATA IN MANUFACTURING & NATURAL RESOURCES

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

12 CHAPTER 12: BIG DATA IN WEB, MEDIA & ENTERTAINMENT

12.1 Overview & Investment Potential
12.2 Key Applications
  12.2.1 Audience & Advertising Optimization
  12.2.2 Channel Optimization
  12.2.3 Recommendation Engines
  12.2.4 Optimized Search
  12.2.5 Live Sports Event Analytics
  12.2.6 Outsourcing Big Data Analytics to Other Verticals
12.3 Case Studies
  12.3.1 NFL (National Football League): Improving Stadium Experience with Big Data
  12.3.2 Walt Disney Company: Enhancing Theme Park Experience with Big Data
  12.3.3 Baidu: Reshaping Search Capabilities with Big Data
  12.3.4 Constant Contact: Effective Marketing with Big Data

13 CHAPTER 13: BIG DATA IN PUBLIC SAFETY & HOMELAND SECURITY

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

14 CHAPTER 14: BIG DATA IN PUBLIC SERVICES

14.1 Overview & Investment Potential
14.2 Key Applications
  14.2.1 Public Sentiment Analysis
  14.2.2 Tax Collection & Fraud Detection
  14.2.3 Economic Analysis
14.3 Case Studies
  14.3.1 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data
  14.3.2 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data
  14.3.3 City of Chicago: Improving Government Productivity with Big Data
  14.3.4 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data
  14.3.5 Ambulance Victoria: Improving Patient Survival Rates with Big Data

15 CHAPTER 15: BIG DATA IN RETAIL, WHOLESALE & HOSPITALITY

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

16 CHAPTER 16: BIG DATA IN TELECOMMUNICATIONS

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

17 CHAPTER 17: BIG DATA IN UTILITIES & ENERGY

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

18 CHAPTER 18: BIG DATA INDUSTRY ROADMAP & VALUE CHAIN

18.1 Big Data Industry Roadmap
  18.1.1 2010 – 2013: Initial Hype and the Rise of Analytics
  18.1.2 2014 – 2017: Emergence of SaaS Based Big Data Solutions
  18.1.3 2018 – 2020: Growing Adoption of Scalable Machine Learning
  18.1.4 2021 & Beyond: Widespread Investments on Cognitive & Personalized Analytics
18.2 The Big Data Value Chain
  18.2.1 Hardware Providers
    18.2.1.1 Storage & Compute Infrastructure Providers
    18.2.1.2 Networking Infrastructure Providers
  18.2.2 Software Providers
    18.2.2.1 Hadoop & Infrastructure Software Providers
    18.2.2.2 SQL & NoSQL Providers
    18.2.2.3 Analytic Platform & Application Software Providers
    18.2.2.4 Cloud Platform Providers
  18.2.3 Professional Services Providers
  18.2.4 End-to-End Solution Providers
  18.2.5 Vertical Enterprises

19 CHAPTER 19: STANDARDIZATION & REGULATORY INITIATIVES

19.1 CSCC (Cloud Standards Customer Council) – Big Data Working Group
19.2 NIST (National Institute of Standards and Technology) – Big Data Working Group
19.3 OASIS –Technical Committees
19.4 ODaF (Open Data Foundation)
19.5 Open Data Center Alliance
19.6 CSA (Cloud Security Alliance) – Big Data Working Group
19.7 ITU (International Telecommunications Union)
19.8 ISO (International Organization for Standardization) and Others

20 CHAPTER 20: MARKET ANALYSIS & FORECASTS

20.1 Global Outlook of the Big Data Market
20.2 Submarket Segmentation
  20.2.1 Storage and Compute Infrastructure
  20.2.2 Networking Infrastructure
  20.2.3 Hadoop & Infrastructure Software
  20.2.4 SQL
  20.2.5 NoSQL
  20.2.6 Analytic Platforms & Applications
  20.2.7 Cloud Platforms
  20.2.8 Professional Services
20.3 Vertical Market Segmentation
  20.3.1 Automotive, Aerospace & Transportation
  20.3.2 Banking & Securities
  20.3.3 Defense & Intelligence
  20.3.4 Education
  20.3.5 Healthcare & Pharmaceutical
  20.3.6 Smart Cities & Intelligent Buildings
  20.3.7 Insurance
  20.3.8 Manufacturing & Natural Resources
  20.3.9 Media & Entertainment
  20.3.10 Public Safety & Homeland Security
  20.3.11 Public Services
  20.3.12 Retail, Wholesale & Hospitality
  20.3.13 Telecommunications
  20.3.14 Utilities & Energy
  20.3.15 Other Sectors
20.4 Regional Outlook
20.5 Asia Pacific
  20.5.1 Country Level Segmentation
  20.5.2 Australia
  20.5.3 China
  20.5.4 India
  20.5.5 Indonesia
  20.5.6 Japan
  20.5.7 Malaysia
  20.5.8 Pakistan
  20.5.9 Philippines
  20.5.10 Singapore
  20.5.11 South Korea
  20.5.12 Taiwan
  20.5.13 Thailand
  20.5.14 Rest of Asia Pacific
20.6 Eastern Europe
  20.6.1 Country Level Segmentation
  20.6.2 Czech Republic
  20.6.3 Poland
  20.6.4 Russia
  20.6.5 Rest of Eastern Europe
20.7 Latin & Central America
  20.7.1 Country Level Segmentation
  20.7.2 Argentina
  20.7.3 Brazil
  20.7.4 Mexico
  20.7.5 Rest of Latin & Central America
20.8 Middle East & Africa
  20.8.1 Country Level Segmentation
  20.8.2 Israel
  20.8.3 Qatar
  20.8.4 Saudi Arabia
  20.8.5 South Africa
  20.8.6 UAE
  20.8.7 Rest of the Middle East & Africa
20.9 North America
  20.9.1 Country Level Segmentation
  20.9.2 Canada
  20.9.3 USA
20.10 Western Europe
  20.10.1 Country Level Segmentation
  20.10.2 Denmark
  20.10.3 Finland
  20.10.4 France
  20.10.5 Germany
  20.10.6 Italy
  20.10.7 Netherlands
  20.10.8 Norway
  20.10.9 Spain
  20.10.10 Sweden
  20.10.11 UK
  20.10.12 Rest of Western Europe

21 CHAPTER 21: VENDOR LANDSCAPE

21.1 1010data
21.2 Accenture
21.3 Actian Corporation
21.4 Actuate Corporation
21.5 Adaptive Insights
21.6 Advizor Solutions
21.7 AeroSpike
21.8 AFS Technologies
21.9 Alpine Data Labs
21.10 Alteryx
21.11 Altiscale
21.12 Antivia
21.13 Arcplan
21.14 Attivio
21.15 Automated Insights
21.16 AWS (Amazon Web Services)
21.17 Ayasdi
21.18 Basho
21.19 BeyondCore
21.20 Birst
21.21 Bitam
21.22 Board International
21.23 Booz Allen Hamilton
21.24 Capgemini
21.25 Cellwize
21.26 Centrifuge Systems
21.27 CenturyLink
21.28 Chartio
21.29 Cisco Systems
21.30 ClearStory Data
21.31 Cloudera
21.32 Comptel
21.33 Concurrent
21.34 Contexti
21.35 Couchbase
21.36 CSC (Computer Science Corporation)
21.37 DataHero
21.38 Datameer
21.39 DataRPM
21.40 DataStax
21.41 Datawatch Corporation
21.42 DDN (DataDirect Network)
21.43 Decisyon
21.44 Dell
21.45 Deloitte
21.46 Denodo Technologies
21.47 Digital Reasoning
21.48 Dimensional Insight
21.49 Domo
21.50 Dundas Data Visualization
21.51 Eligotech
21.52 EMC Corporation
21.53 Engineering Group (Engineering Ingegneria Informatica)
21.54 eQ Technologic
21.55 Facebook
21.56 FICO
21.57 Fractal Analytics
21.58 Fujitsu
21.59 Fusion-io
21.60 GE (General Electric)
21.61 GoodData Corporation
21.62 Google
21.63 Guavus
21.64 HDS (Hitachi Data Systems)
21.65 Hortonworks
21.66 HPE (Hewlett Packard Enterprise)
21.67 IBM
21.68 iDashboards
21.69 Incorta
21.70 InetSoft Technology Corporation
21.71 InfiniDB
21.72 Infor
21.73 Informatica Corporation
21.74 Information Builders
21.75 Intel
21.76 Jedox
21.77 Jinfonet Software
21.78 Juniper Networks
21.79 Knime
21.80 Kofax
21.81 Kognitio
21.82 L-3 Communications
21.83 Lavastorm Analytics
21.84 Logi Analytics
21.85 Looker Data Sciences
21.86 LucidWorks
21.87 Maana
21.88 Manthan Software Services
21.89 MapR
21.90 MarkLogic
21.91 MemSQL
21.92 Microsoft
21.93 MicroStrategy
21.94 MongoDB (formerly 10gen)
21.95 Mu Sigma
21.96 NTT Data
21.97 Neo Technology
21.98 NetApp
21.99 Nutonian
21.100 OpenText Corporation
21.101 Opera Solutions
21.102 Oracle
21.103 Palantir Technologies
21.104 Panorama Software
21.105 ParStream
21.106 Pentaho
21.107 Phocas
21.108 Pivotal Software
21.109 Platfora
21.110 Prognoz
21.111 PwC
21.112 Pyramid Analytics
21.113 Qlik
21.114 Quantum Corporation
21.115 Qubole
21.116 Rackspace
21.117 RapidMiner
21.118 Recorded Future
21.119 RJMetrics
21.120 Salesforce.com
21.121 Sailthru
21.122 Salient Management Company
21.123 SAP
21.124 SAS Institute
21.125 SGI
21.126 SiSense
21.127 Software AG
21.128 Splice Machine
21.129 Splunk
21.130 Sqrrl
21.131 Strategy Companion
21.132 Supermicro
21.133 Syncsort
21.134 SynerScope
21.135 Tableau Software
21.136 Talend
21.137 Targit
21.138 TCS (Tata Consultancy Services)
21.139 Teradata
21.140 Think Big Analytics
21.141 ThoughtSpot
21.142 TIBCO Software
21.143 Tidemark
21.144 VMware (EMC Subsidiary)
21.145 WiPro
21.146 Yellowfin International
21.147 Zendesk
21.148 Zettics
21.149 Zoomdata
21.150 Zucchetti

22 CHAPTER 22: CONCLUSION & STRATEGIC RECOMMENDATIONS

22.1 Big Data Technology: Beyond Data Capture & Analytics
22.2 Transforming IT from a Cost Center to a Profit Center
22.3 Can Privacy Implications Hinder Success?
22.4 Will Regulation have a Negative Impact on Big Data Investments?
22.5 Battling Organization & Data Silos
22.6 Software vs. Hardware Investments
22.7 Vendor Share: Who Leads the Market?
22.8 Big Data Driving Wider IT Industry Investments
22.9 Assessing the Impact of IoT & M2M
22.10 Recommendations
  22.10.1 Big Data Hardware, Software & Professional Services Providers
  22.10.2 Enterprises

SNS Research's latest report indicates that global spending on Big Data technology is expected to reach nearly $30 Billion by the end of 2014.  Originally used as a term to describe datasets whose size is beyond the ability of traditional databases, the scope of Big Data 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 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 $30 Billion in 2014 alone. These investments are further expected to grow at a CAGR of 17% over the next 6 years. 

The “Big Data Market: 2014 – 2020” 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 2014 through to 2020. Historical figures are also presented for 2010, 2011, 2012 and 2013. The forecasts are further segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and 34 countries.

The material was prepared in June, 2014.

LIST OF FIGURES

Figure 1: Reactive vs. Proactive Analytics
Figure 2: Big Data Industry Roadmap
Figure 3: The Big Data Value Chain
Figure 4: Global Big Data Revenue: 2016 - 2030 ($ Million)
Figure 5: Global Big Data Revenue by Submarket: 2016 - 2030 ($ Million)
Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2016 - 2030 ($ Million)
Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2016 - 2030 ($ Million)
Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2016 - 2030 ($ Million)
Figure 9: Global Big Data SQL Submarket Revenue: 2016 - 2030 ($ Million)
Figure 10: Global Big Data NoSQL Submarket Revenue: 2016 - 2030 ($ Million)
Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2016 - 2030 ($ Million)
Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2016 - 2030 ($ Million)
Figure 13: Global Big Data Professional Services Submarket Revenue: 2016 - 2030 ($ Million)
Figure 14: Global Big Data Revenue by Vertical Market: 2016 - 2030 ($ Million)
Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2016 - 2030 ($ Million)
Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2016 - 2030 ($ Million)
Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2016 - 2030 ($ Million)
Figure 18: Global Big Data Revenue in the Education Sector: 2016 - 2030 ($ Million)
Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2016 - 2030 ($ Million)
Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2016 - 2030 ($ Million)
Figure 21: Global Big Data Revenue in the Insurance Sector: 2016 - 2030 ($ Million)
Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2016 - 2030 ($ Million)
Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2016 - 2030 ($ Million)
Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2016 - 2030 ($ Million)
Figure 25: Global Big Data Revenue in the Public Services Sector: 2016 - 2030 ($ Million)
Figure 26: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2016 - 2030 ($ Million)
Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2016 - 2030 ($ Million)
Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2016 - 2030 ($ Million)
Figure 29: Global Big Data Revenue in Other Vertical Sectors: 2016 - 2030 ($ Million)
Figure 30: Big Data Revenue by Region: 2016 - 2030 ($ Million)
Figure 31: Asia Pacific Big Data Revenue: 2016 - 2030 ($ Million)
Figure 32: Asia Pacific Big Data Revenue by Country: 2016 - 2030 ($ Million)
Figure 33: Australia Big Data Revenue: 2016 - 2030 ($ Million)
Figure 34: China Big Data Revenue: 2016 - 2030 ($ Million)
Figure 35: India Big Data Revenue: 2016 - 2030 ($ Million)
Figure 36: Indonesia Big Data Revenue: 2016 - 2030 ($ Million)
Figure 37: Japan Big Data Revenue: 2016 - 2030 ($ Million)
Figure 38: Malaysia Big Data Revenue: 2016 - 2030 ($ Million)
Figure 39: Pakistan Big Data Revenue: 2016 - 2030 ($ Million)
Figure 40: Philippines Big Data Revenue: 2016 - 2030 ($ Million)
Figure 41: Singapore Big Data Revenue: 2016 - 2030 ($ Million)
Figure 42: South Korea Big Data Revenue: 2016 - 2030 ($ Million)
Figure 43: Taiwan Big Data Revenue: 2016 - 2030 ($ Million)
Figure 44: Thailand Big Data Revenue: 2016 - 2030 ($ Million)
Figure 45: Big Data Revenue in the Rest of Asia Pacific: 2016 - 2030 ($ Million)
Figure 46: Eastern Europe Big Data Revenue: 2016 - 2030 ($ Million)
Figure 47: Eastern Europe Big Data Revenue by Country: 2016 - 2030 ($ Million)
Figure 48: Czech Republic Big Data Revenue: 2016 - 2030 ($ Million)
Figure 49: Poland Big Data Revenue: 2016 - 2030 ($ Million)
Figure 50: Russia Big Data Revenue: 2016 - 2030 ($ Million)
Figure 51: Big Data Revenue in the Rest of Eastern Europe: 2016 - 2030 ($ Million)
Figure 52: Latin & Central America Big Data Revenue: 2016 - 2030 ($ Million)
Figure 53: Latin & Central America Big Data Revenue by Country: 2016 - 2030 ($ Million)
Figure 54: Argentina Big Data Revenue: 2016 - 2030 ($ Million)
Figure 55: Brazil Big Data Revenue: 2016 - 2030 ($ Million)
Figure 56: Mexico Big Data Revenue: 2016 - 2030 ($ Million)
Figure 57: Big Data Revenue in the Rest of Latin & Central America: 2016 - 2030 ($ Million)
Figure 58: Middle East & Africa Big Data Revenue: 2016 - 2030 ($ Million)
Figure 59: Middle East & Africa Big Data Revenue by Country: 2016 - 2030 ($ Million)
Figure 60: Israel Big Data Revenue: 2016 - 2030 ($ Million)
Figure 61: Qatar Big Data Revenue: 2016 - 2030 ($ Million)
Figure 62: Saudi Arabia Big Data Revenue: 2016 - 2030 ($ Million)
Figure 63: South Africa Big Data Revenue: 2016 - 2030 ($ Million)
Figure 64: UAE Big Data Revenue: 2016 - 2030 ($ Million)
Figure 65: Big Data Revenue in the Rest of the Middle East & Africa: 2016 - 2030 ($ Million)
Figure 66: North America Big Data Revenue: 2016 - 2030 ($ Million)
Figure 67: North America Big Data Revenue by Country: 2016 - 2030 ($ Million)
Figure 68: Canada Big Data Revenue: 2016 - 2030 ($ Million)
Figure 69: USA Big Data Revenue: 2016 - 2030 ($ Million)
Figure 70: Western Europe Big Data Revenue: 2016 - 2030 ($ Million)
Figure 71: Western Europe Big Data Revenue by Country: 2016 - 2030 ($ Million)
Figure 72: Denmark Big Data Revenue: 2016 - 2030 ($ Million)
Figure 73: Finland Big Data Revenue: 2016 - 2030 ($ Million)
Figure 74: France Big Data Revenue: 2016 - 2030 ($ Million)
Figure 75: Germany Big Data Revenue: 2016 - 2030 ($ Million)
Figure 76: Italy Big Data Revenue: 2016 - 2030 ($ Million)
Figure 77: Netherlands Big Data Revenue: 2016 - 2030 ($ Million)
Figure 78: Norway Big Data Revenue: 2016 - 2030 ($ Million)
Figure 79: Spain Big Data Revenue: 2016 - 2030 ($ Million)
Figure 80: Sweden Big Data Revenue: 2016 - 2030 ($ Million)
Figure 81: UK Big Data Revenue: 2016 - 2030 ($ Million)
Figure 82: Big Data Revenue in the Rest of Western Europe: 2016 - 2030 ($ Million)
Figure 83: Global Big Data Revenue by Hardware, Software & Professional Services: 2016 – 2030 ($ Million)
Figure 84: Big Data Vendor Market Share (%)
Figure 85: Global IT Expenditure Driven by Big Data Investments: 2016 - 2030 ($ Million)
Figure 86: Global M2M Connections by Access Technology: 2016 – 2030 (Millions)
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The Big Data Market: 2016 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals and Forecasts
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