Date: Wed, 8 Feb 2017 23:26:35 -0800 From: "Andy Silva" <andy.silva@snsresearchreports.com> To: freebsd-ppc@freebsd.org Subject: The Big Data Market: 2016 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts (Report) Message-ID: <84524064472481066513342@Ankur>
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The Big Data Market: 2016 =96 2030 - Opportunities, Challenges, Strategies,= Industry Verticals and Forecasts (Report) Hello Let me offer you the latest SNS Research report to you and your team, " Big= Data Market: 2016 =96 2030 =96 Opportunities, Challenges, Strategies, Indu= stry Verticals & Forecasts" Below is the report highlight and if you like I= can send you sample pages for your details inside. =20 Our reports are compiled with primary and secondary informations to produce= an overall industry outlook. Report Information: Number of Pages: 390 Number of Tables and Figures: 86 Key Questions Answered: The report provides answers to the following key questions: How big is the Big Data ecosystem=3F How is the ecosystem evolving by segment and region=3F What will the market size be in 2020 and at what rate will it grow=3F What trends, challenges and barriers are influencing its growth=3F Who are the key Big Data software, hardware and services vendors and what a= re their strategies=3F How much are vertical enterprises investing in Big Data=3F What opportunities exist for Big Data analytics=3F Which countries and verticals will see the highest percentage of Big Data i= nvestments=3F Key Findings: The report has the following key findings: In 2016, Big Data vendors will pocket over $46 Billion from hardware, softw= are and professional services revenues. Big Data investments are further expected to grow at a CAGR of 12% over the= next four years, eventually accounting for over $72 Billion by the end of = 2020. The market is ripe for acquisitions of pure-play Big Data startups, as comp= etition 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 professio= nal services in terms of revenue. Going forward, software vendors, particularly those in the Big Data analyti= cs segment, are expected to significantly increase their stake in the Big D= ata market. By the end of 2020, the author expects Big Data software revenue to exceed = hardware investments by over $7 Billion. 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 150 Big Data ecosystem players Strategic recommendations for Big Data hardware, software and professional = services vendors and enterprises Market analysis and forecasts from 2016 till 2030 Forecast Segmentation: Market forecasts and historical figures are provided for each of the follow= ing submarkets and their categories: Hardware, Software & Professional Services=20 Hardware=20 Software=20 Professional Services Horizontal Submarkets=20 Storage & Compute Infrastructure=20 Networking Infrastructure=20 Hadoop & Infrastructure Software=20 SQL=20 NoSQL=20 Analytic Platforms & Applications=20 Cloud Platforms=20 Professional Services Vertical Submarkets=20 Automotive, Aerospace & Transportation=20 Banking & Securities=20 Defense & Intelligence=20 Education=20 Healthcare & Pharmaceutical=20 Smart Cities & Intelligent Buildings=20 Insurance=20 Manufacturing & Natural Resources=20 Web, Media & Entertainment=20 Public Safety & Homeland Security=20 Public Services=20 Retail & Hospitality=20 Telecommunications=20 Utilities & Energy=20 Wholesale Trade=20 Others Regional Markets=20 Asia Pacific=20 Eastern Europe=20 Latin & Central America=20 Middle East & Africa=20 North America=20 Western Europe Country Markets=20 Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finla= nd, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mex= ico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Sau= di Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Tha= iland, UAE, UK, USA =20 Report Pricing: =20 Single User License: USD 2,500 Company Wide License: USD 3,500 =20 Ordering Process: =20 Please provide the following information: Report Title - Big Data Market: 2016 =96 2030 Report License - (Single User/Company Wide) Name - Email - Job Title - Company - Invoice Address - Please contact me if you have any questions, or wish to purchase a copy. Ta= ble of contents, List of figures and List of companies mentioned in report = are given below for more inside. I look forward to hearing from you. =20 Kind Regards =20 Andy Silva Marketing Executive Signals and Systems Telecom andy.silva@snscommunication.com _________________________________________________________________________ Table of Contents: =20 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 =20 2: An Overview of Big Data 2.1 What is Big Data=3F 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 =20 3: Big Data Analytics 3.1 What are Big Data Analytics=3F 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 =20 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 Bi= g Data =20 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 =20 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 Th= reats 6.3.4 Chinese Ministry of State Security: Predictive Policing with Big Data 6.3.5 French DGSE (General Directorate for External Security): Enhancing In= telligence with Big Data =20 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 =20 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 =20 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=92s Most Intelligent Building wit= h Big Data =20 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 =20 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 =20 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 Bi= g 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 =20 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 P= hysical 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 =20 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 Re= venue 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 =20 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 =20 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 =20 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 =20 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 An= alytics 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 =20 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 Worki= ng 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 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 =20 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 =20 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=3F 22.4 Will Regulation have a Negative Impact on Big Data Investments=3F 22.5 Battling Organization & Data Silos 22.6 Software vs. Hardware Investments 22.7 Vendor Share: Who Leads the Market=3F 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 =20 List of Figures: =20 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 Reve= nue: 2016 - 2030 ($ Million) Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2016= - 2030 ($ Million) Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenu= e: 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 Reve= nue: 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 ($ Milli= on) Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transport= ation 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: 20= 16 - 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 Secto= r: 2016 - 2030 ($ Million) Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildi= ngs 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: 201= 6 - 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 - 20= 30 ($ Million) Figure 26: Global Big Data Revenue in the Retail, Wholesale & Hospitality S= ector: 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 ($ Mil= lion) Figure 46: Eastern Europe Big Data Revenue: 2016 - 2030 ($ Million) Figure 47: Eastern Europe Big Data Revenue by Country: 2016 - 2030 ($ Milli= on) 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 ($ M= illion) 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 ($ Millio= n) 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 ($ Milli= on) 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 ($ M= illion) Figure 83: Global Big Data Revenue by Hardware, Software & Professional Ser= vices: 2016 - 2030 ($ Million) Figure 84: Big Data Vendor Market Share (%) Figure 85: Global IT Expenditure Driven by Big Data Investments: 2016 - 203= 0 ($ Million) Figure 86: Global M2M Connections by Access Technology: 2016 - 2030 (Millio= ns) =20 Thank you once again and looking forward to hearing from you. =20 Kind Regards =20 Andy Silva Marketing Executive Signals and Systems Telecom andy.silva@snscommunication.com =20 =20 To unsubscribe send an email with unsubscribe in the subject line to: remov= e@snsreports.com =20
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