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Published: Jun, 2016 | Pages:
390 | Publisher: SNS Research
Industry: Telecommunications | Report Format: Electronic (PDF)
“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. Topics Covered 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 Historical Revenue & Forecast Segmentation Market forecasts and historical revenue figures are provided for each of the following submarkets and their subcategories: Hardware, Software & Professional Services - Hardware - Software - Professional Services Horizontal Submarkets - Storage & Compute Infrastructure - Networking Infrastructure - Hadoop & Infrastructure Software - SQL - NoSQL - Analytic Platforms & Applications - Cloud Platforms - Professional Services Vertical Submarkets - Automotive, Aerospace & Transportation - Banking & Securities - Defense & Intelligence - Education - Healthcare & Pharmaceutical - Smart Cities & Intelligent Buildings - Insurance - Manufacturing & Natural Resources - Web, Media & Entertainment - Public Safety & Homeland Security - Public Services - Retail, 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 Key Questions Answered The report provides answers to the following key questions: - How big is the Big Data ecosystem? - How is the ecosystem evolving by segment and region? - What will the market size be in 2020 and at what rate will it grow? - What trends, challenges and barriers are influencing its growth? - Who are the key Big Data software, hardware and services vendors and what are their strategies? - How much are vertical enterprises investing in Big Data? - What opportunities exist for Big Data analytics? - Which countries and verticals will see the highest percentage of Big Data investments? Key Findings The report has the following key findings: - In 2016, Big Data vendors will pocket over $46 Billion from hardware, software 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 competition heats up between IT incumbents. - Nearly every large scale IT vendor maintains a Big Data portfolio. - At present, the market is largely dominated by hardware sales and professional services in terms of revenue. - Going forward, software vendors, particularly those in the Big Data analytics segment, are expected to significantly increase their stake in the Big Data market. - By the end of 2020, SNS Research expects Big Data software revenue to exceed hardware investments by over $7 Billion. List of Companies Mentioned 1010data Accel Partners Accenture Actian Corporation Actuate Corporation Adaptive Insights adMarketplace Adobe ADP Advizor Solutions AeroSpike AFS Technologies Alameda County Social Services Agency AlchemyDB Aldeasa Alpine Data Labs Alteryx Altiscale Altosoft Amazon.com Ambulance Victoria AMD AnalyticsIQ Antic Entertainment Antivia AOL Apple AppNexus Arcplan Ascendas AT&T Attivio Automated Insights AutoZone Avvasi AWS (Amazon Web Services) Axiata Group Ayasdi BAE Systems Baidu Bank of America Basho Beeline Kazakhstan Betfair BeyondCore Birst Bitam BlueKai Bluelock BMC Software BMW Board International Boeing Booz Allen Hamilton Box British Gas BT Group Buffalo Studios BurstaBit CaixaTarragona Capgemini CBA (Commonwealth Bank of Australia) Cellwize Centrifuge Systems CenturyLink CETC (China Electronics Technology Group) Chang Chartio Chevron Technology Ventures China Telecom Chinese Ministry of State Security CIA (Central Intelligence Agency) Cisco Systems Citywire ClearStory Data Cloudera Coca-Cola Comptel Concur Concurrent Constant Contact Contexti Coriant Couchbase CSA (Cloud Security Alliance) CSC (Computer Science Corporation) CSCC (Cloud Standards Customer Council) DataHero Datameer DataRPM DataStax Datawatch Corporation DDN (DataDirect Network) Decisyon Dell Deloitte Delta Denodo Technologies Deutsche Bank Digital Reasoning Dimensional Insight Dollar General Domo Dotomi Dow Chemical Company DT (Deutsche Telekom) Dubai Police Dundas Data Visualization eBay Edith Cowen University El Corte Inglés Electronic Arts Eligotech EMC Corporation Engineering Group (Engineering Ingegneria Informatica) eQ Technologic Equifax Ericsson Ernst & Young E-Touch European Space Agency eXelate Experian Facebook FDNY (Fire Department of the City of New York) FedEx Ferguson Enterprises FICO Ford Motor Company Foundation Medicine Fractal Analytics French DGSE (General Directorate for External Security) Fujitsu Fusion-io Gamegos Ganz GE (General Electric) Glasgow City Council Goldman Sachs GoodData Corporation Google Greylock Partners GSK (GlaxoSmithKline) GTRI (Georgia Tech Research Institute) Guavus Hadapt HDS (Hitachi Data Systems) Hortonworks HPE (Hewlett Packard Enterprise) HSBC Group Hyve Solutions IBM iDashboards IEC (International Electrotechnical Commission) Ignition Partners Incorta InetSoft Technology Corporation InfiniDB Infobright Infor Informatica Corporation Information Builders In-Q-Tel Intel Corporation Internap Network Services Corporation Intucell Inversis Banco ISO (International Organization for Standardization) ITT Corporation ITU (International Telecommunications Union) J.P. Morgan Jaspersoft Jedox Jinfonet Software JJ Food Service Johnson & Johnson JPMorgan Chase & Co. Juguettos Juniper Networks Kabam Karmasphere KDDI Kixeye Knime Kobo Kofax Kognitio KPMG KT (Korea Telecom) L-3 Communications L-3 Data Tactics Lavastorm Analytics LG CNS LinkedIn Logi Analytics Logos Technologies Looker Data Sciences LucidWorks Maana Mahindra Satyam Manthan Software Services MapR MarkLogic Marriott International Mayfield fund McDonnell Ventures McGraw Hill Education MediaMind Memphis Police Department MemSQL Meritech Capital Partners Michelin Microsoft MicroStrategy mig33 MongoDB MongoDB (Formerly 10gen) Movistar Mu Sigma Myrrix Nami Media NASA (National Aeronautics and Space Administration) Navteq Neo Technology NetApp NetFlix New York State Department of Taxation and Finance Nexon Nextbio NFL (National Football League) NIST (National Institute of Standards and Technology) North Bridge Northwest Analytics Nottingham Trent University Novartis NSA (National Security Agency) NTT Data NTT DoCoMo Nutonian NYSE (New York Stock Exchange) OASIS ODaF (Open Data Foundation) Ofcom Oncor Electric Delivery Open Data Center Alliance OpenText Corporation Opera Solutions Optimal+ Oracle Corporation Orange Orbitz OTP Bank OVG Real Estate Palantir Technologies Panorama Software ParAccel ParStream Pentaho Pervasive Software Pfizer Phocas Pivotal Software Platfora Playtika Primerica Proctor and Gamble Prognoz Pronovias Purdue University PwC Pyramid Analytics Qlik QPC Quantum Corporation Qubole Quiterian Rackspace RainStor RapidMiner Recorded Future Relational Technology Renault ReNet Tecnologia Rentrak Revolution Analytics RiteAid RJMetrics Robi Axiata Roche Royal Dutch Shell Royal Navy RSA Group Sabre Sailthru Sain Engineering Salesforce.com Salient Management Company Samsung Sanofi SAP SAS Institute Saudi Aramco Energy Ventures Savvis Scoreloop Seagate Technology SGI Shuffle Master Simba Technologies SiSense Skyscanner SmugMug Snapdeal Software AG Sojo Studios SolveDirect Sony Corporation Southern States Cooperative SpagoBI Labs Splice Machine Splunk Spotfire Spotme Sqrrl Starbucks Strategy Companion Supermicro Syncsort SynerScope Tableau Software Talend Tango TapJoy Targit TCS (Tata Consultancy Services) Telefónica Tencent TEOCO Teradata Terracotta Terremark Tesco Thales Group The Hut Group The Knot The Ladders The Trade Desk Think Big Analytics Thomson Reuters ThoughtSpot TIBCO Software Tidemark T-Mobile USA Toyota Motor Corporation TubeMogul Tunewiki U.S. Air Force U.S. Army U.S. CBP (Customs and Border Protection) U.S. Coast Guard U.S. Department of Commerce U.S. DHS (Department of Homeland Security) U.S. ICE (Immigration and Customs Enforcement) U.S. Navy Ubiquisys UBS UIEvolution Umami TV UN (United Nations) Unilever US Xpress Venture Partners Verizon Communications Versant Vertica VIMPELCOM VMware VNG Vodafone Volkswagen Walmart Walt Disney Company WIND Mobile WiPro Xclaim Xyratex Yael Software Yellowfin International Zebra Technologies Zendesk Zettics Zoomdata Zucchetti Zurich Insurance Group Zynga
Table of Contents 1 Chapter 1: Introduction 21 1.1 Executive Summary 21 1.2 Topics Covered 23 1.3 Historical Revenue & Forecast Segmentation 24 1.4 Key Questions Answered 26 1.5 Key Findings 27 1.6 Methodology 28 1.7 Target Audience 29 1.8 Companies & Organizations Mentioned 30 2 Chapter 2: An Overview of Big Data 34 2.1 What is Big Data? 34 2.2 Key Approaches to Big Data Processing 34 2.2.1 Hadoop 35 2.2.2 NoSQL 36 2.2.3 MPAD (Massively Parallel Analytic Databases) 36 2.2.4 In-memory Processing 37 2.2.5 Stream Processing Technologies 37 2.2.6 Spark 38 2.2.7 Other Databases & Analytic Technologies 38 2.3 Key Characteristics of Big Data 39 2.3.1 Volume 39 2.3.2 Velocity 39 2.3.3 Variety 39 2.3.4 Value 40 2.4 Market Growth Drivers 41 2.4.1 Awareness of Benefits 41 2.4.2 Maturation of Big Data Platforms 41 2.4.3 Continued Investments by Web Giants, Governments & Enterprises 42 2.4.4 Growth of Data Volume, Velocity & Variety 42 2.4.5 Vendor Commitments & Partnerships 42 2.4.6 Technology Trends Lowering Entry Barriers 43 2.5 Market Barriers 43 2.5.1 Lack of Analytic Specialists 43 2.5.2 Uncertain Big Data Strategies 43 2.5.3 Organizational Resistance to Big Data Adoption 44 2.5.4 Technical Challenges: Scalability & Maintenance 44 2.5.5 Security & Privacy Concerns 44 3 Chapter 3: Big Data Analytics 46 3.1 What are Big Data Analytics? 46 3.2 The Importance of Analytics 46 3.3 Reactive vs. Proactive Analytics 47 3.4 Customer vs. Operational Analytics 48 3.5 Technology & Implementation Approaches 48 3.5.1 Grid Computing 48 3.5.2 In-Database Processing 49 3.5.3 In-Memory Analytics 49 3.5.4 Machine Learning & Data Mining 49 3.5.5 Predictive Analytics 50 3.5.6 NLP (Natural Language Processing) 50 3.5.7 Text Analytics 51 3.5.8 Visual Analytics 52 3.5.9 Social Media, IT & Telco Network Analytics 52 4 Chapter 4: Big Data in Automotive, Aerospace & Transportation 53 4.1 Overview & Investment Potential 53 4.2 Key Applications 53 4.2.1 Warranty Analytics for Automotive OEMs 54 4.2.2 Predictive Aircraft Maintenance & Fuel Optimization 54 4.2.3 Air Traffic Control 55 4.2.4 Transport Fleet Optimization 55 4.3 Case Studies 55 4.3.1 Boeing: Making Flying More Efficient with Big Data 56 4.3.2 BMW: Eliminating Defects in New Vehicle Models with Big Data 57 4.3.3 Toyota Motor Corporation: Powering Smart Cars with Big Data 58 4.3.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data 59 5 Chapter 5: Big Data in Banking & Securities 60 5.1 Overview & Investment Potential 60 5.2 Key Applications 60 5.2.1 Customer Retention & Personalized Product Offering 60 5.2.2 Risk Management 61 5.2.3 Fraud Detection 61 5.2.4 Credit Scoring 61 5.3 Case Studies 61 5.3.1 HSBC Group: Avoiding Regulatory Penalties with Big Data 62 5.3.2 JPMorgan Chase & Co.: Improving Business Processes with Big Data 63 5.3.3 OTP Bank: Reducing Loan Defaults with Big Data 64 5.3.4 CBA (Commonwealth Bank of Australia): Providing Personalized Services with Big Data 65 6 Chapter 6: Big Data in Defense & Intelligence 66 6.1 Overview & Investment Potential 66 6.2 Key Applications 66 6.2.1 Intelligence Gathering 66 6.2.2 Battlefield Analytics 67 6.2.3 Energy Saving Opportunities in the Battlefield 67 6.2.4 Preventing Injuries on the Battlefield 68 6.3 Case Studies 69 6.3.1 U.S. Air Force: Providing Actionable Intelligence to Warfighters with Big Data 69 6.3.2 Royal Navy: Empowering Submarine Warfare with Big Data 70 6.3.3 NSA (National Security Agency): Capitalizing on Big Data to Detect Threats 71 6.3.4 Chinese Ministry of State Security: Predictive Policing with Big Data 72 6.3.5 French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data 73 7 Chapter 7: Big Data in Education 75 7.1 Overview & Investment Potential 75 7.2 Key Applications 75 7.2.1 Information Integration 75 7.2.2 Identifying Learning Patterns 76 7.2.3 Enabling Student-Directed Learning 76 7.3 Case Studies 76 7.3.1 Purdue University: Ensuring Successful Higher Education Outcomes with Big Data 77 7.3.2 Nottingham Trent University: Successful Student Outcomes with Big Data 78 7.3.3 Edith Cowen University: Increasing Student Retention with Big Data 79 8 Chapter 8: Big Data in Healthcare & Pharma 80 8.1 Overview & Investment Potential 80 8.2 Key Applications 80 8.2.1 Managing Population Health Efficiently 80 8.2.2 Improving Patient Care with Medical Data Analytics 81 8.2.3 Improving Clinical Development & Trials 81 8.2.4 Drug Development: Improving Time to Market 81 8.3 Case Studies 82 8.3.1 Novartis: Digitizing Healthcare with Big Data 82 8.3.2 GSK (GlaxoSmithKline): Accelerating Drug Discovering with Big Data 83 8.3.3 Pfizer: Developing Effective and Targeted Therapies with Big Data 84 8.3.4 Roche: Personalizing Healthcare with Big Data 85 8.3.5 Sanofi: Proactive Diabetes Care with Big Data 86 9 Chapter 9: Big Data in Smart Cities & Intelligent Buildings 88 9.1 Overview & Investment Potential 88 9.2 Key Applications 88 9.2.1 Energy Optimization & Fault Detection 88 9.2.2 Intelligent Building Analytics 89 9.2.3 Urban Transportation Management 89 9.2.4 Optimizing Energy Production 89 9.2.5 Water Management 90 9.2.6 Urban Waste Management 90 9.3 Case Studies 90 9.3.1 Singapore: Building a Smart Nation with Big Data 90 9.3.2 Glasgow City Council: Promoting Smart City Efforts with Big Data 92 9.3.3 OVG Real Estate: Powering the World’s Most Intelligent Building with Big Data 93 10 Chapter 10: Big Data in Insurance 94 10.1 Overview & Investment Potential 94 10.2 Key Applications 94 10.2.1 Claims Fraud Mitigation 94 10.2.2 Customer Retention & Profiling 95 10.2.3 Risk Management 95 10.3 Case Studies 95 10.3.1 Zurich Insurance Group: Enhancing Risk Management with Big Data 95 10.3.2 RSA Group: Improving Customer Relations with Big Data 97 10.3.3 Primerica: Improving Insurance Sales Force Productivity with Big Data 98 11 Chapter 11: Big Data in Manufacturing & Natural Resources 99 11.1 Overview & Investment Potential 99 11.2 Key Applications 99 11.2.1 Asset Maintenance & Downtime Reduction 99 11.2.2 Quality & Environmental Impact Control 100 11.2.3 Optimized Supply Chain 100 11.2.4 Exploration & Identification of Natural Resources 100 11.3 Case Studies 101 11.3.1 Intel Corporation: Cutting Manufacturing Costs with Big Data 101 11.3.2 Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data 102 11.3.3 Michelin: Improving the Efficiency of Supply Chain and Manufacturing with Big Data 103 11.3.4 Brunei: Saving Natural Resources with Big Data 104 12 Chapter 12: Big Data in Web, Media & Entertainment 105 12.1 Overview & Investment Potential 105 12.2 Key Applications 105 12.2.1 Audience & Advertising Optimization 106 12.2.2 Channel Optimization 106 12.2.3 Recommendation Engines 106 12.2.4 Optimized Search 106 12.2.5 Live Sports Event Analytics 107 12.2.6 Outsourcing Big Data Analytics to Other Verticals 107 12.3 Case Studies 107 12.3.1 NFL (National Football League): Improving Stadium Experience with Big Data 107 12.3.2 Walt Disney Company: Enhancing Theme Park Experience with Big Data 109 12.3.3 Baidu: Reshaping Search Capabilities with Big Data 110 12.3.4 Constant Contact: Effective Marketing with Big Data 111 13 Chapter 13: Big Data in Public Safety & Homeland Security 112 13.1 Overview & Investment Potential 112 13.2 Key Applications 112 13.2.1 Cyber Crime Mitigation 113 13.2.2 Crime Prediction Analytics 113 13.2.3 Video Analytics & Situational Awareness 113 13.3 Case Studies 114 13.3.1 U.S. DHS (Department of Homeland Security): Identifying Threats to Physical and Network Infrastructure with Big Data 114 13.3.2 Dubai Police: Locating Wanted Vehicles More Efficiently with Big Data 115 13.3.3 Memphis Police Department: Crime Reduction with Big Data 116 14 Chapter 14: Big Data in Public Services 117 14.1 Overview & Investment Potential 117 14.2 Key Applications 117 14.2.1 Public Sentiment Analysis 117 14.2.2 Tax Collection & Fraud Detection 118 14.2.3 Economic Analysis 118 14.3 Case Studies 118 14.3.1 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data 118 14.3.2 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data 119 14.3.3 City of Chicago: Improving Government Productivity with Big Data 120 14.3.4 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data 121 14.3.5 Ambulance Victoria: Improving Patient Survival Rates with Big Data 122 15 Chapter 15: Big Data in Retail, Wholesale & Hospitality 124 15.1 Overview & Investment Potential 124 15.2 Key Applications 124 15.2.1 Customer Sentiment Analysis 125 15.2.2 Customer & Branch Segmentation 125 15.2.3 Price Optimization 125 15.2.4 Personalized Marketing 125 15.2.5 Optimizing & Monitoring the Supply Chain 126 15.2.6 In-field Sales Analytics 126 15.3 Case Studies 126 15.3.1 Walmart: Making Smarter Stocking Decision with Big Data 127 15.3.2 Tesco: Reducing Supermarket Energy Bills with Big Data 128 15.3.3 Marriott International: Elevating Guest Services with Big Data 129 15.3.4 JJ Food Service: Predictive Wholesale Shopping Lists with Big Data 130 16 Chapter 16: Big Data in Telecommunications 131 16.1 Overview & Investment Potential 131 16.2 Key Applications 131 16.2.1 Network Performance & Coverage Optimization 131 16.2.2 Customer Churn Prevention 132 16.2.3 Personalized Marketing 132 16.2.4 Tailored Location Based Services 132 16.2.5 Fraud Detection 132 16.3 Case Studies 133 16.3.1 BT Group: Hunting Down Nuisance Callers with Big Data 133 16.3.2 AT&T: Smart Network Management with Big Data 134 16.3.3 T-Mobile USA: Cutting Down Churn Rate with Big Data 135 16.3.4 TEOCO: Helping Service Providers Save Millions with Big Data 136 16.3.5 WIND Mobile: Optimizing Video Quality with Big Data 137 16.3.6 Coriant: SaaS Based Analytics with Big Data 138 17 Chapter 17: Big Data in Utilities & Energy 139 17.1 Overview & Investment Potential 139 17.2 Key Applications 139 17.2.1 Customer Retention 139 17.2.2 Forecasting Energy 140 17.2.3 Billing Analytics 140 17.2.4 Predictive Maintenance 140 17.2.5 Maximizing the Potential of Drilling 140 17.2.6 Production Optimization 141 17.3 Case Studies 141 17.3.1 Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data 141 17.3.2 British Gas: Improving Customer Service with Big Data 142 17.3.3 Oncor Electric Delivery: Intelligent Power Grid Management with Big Data 143 18 Chapter 18: Big Data Industry Roadmap & Value Chain 144 18.1 Big Data Industry Roadmap 144 18.1.1 2010 – 2013: Initial Hype and the Rise of Analytics 144 18.1.2 2014 – 2017: Emergence of SaaS Based Big Data Solutions 145 18.1.3 2018 – 2020: Growing Adoption of Scalable Machine Learning 146 18.1.4 2021 & Beyond: Widespread Investments on Cognitive & Personalized Analytics 146 18.2 The Big Data Value Chain 147 18.2.1 Hardware Providers 147 18.2.1.1 Storage & Compute Infrastructure Providers 147 18.2.1.2 Networking Infrastructure Providers 148 18.2.2 Software Providers 149 18.2.2.1 Hadoop & Infrastructure Software Providers 149 18.2.2.2 SQL & NoSQL Providers 149 18.2.2.3 Analytic Platform & Application Software Providers 149 18.2.2.4 Cloud Platform Providers 150 18.2.3 Professional Services Providers 150 18.2.4 End-to-End Solution Providers 150 18.2.5 Vertical Enterprises 150 19 Chapter 19: Standardization & Regulatory Initiatives 151 19.1 CSCC (Cloud Standards Customer Council) – Big Data Working Group 151 19.2 NIST (National Institute of Standards and Technology) – Big Data Working Group 152 19.3 OASIS –Technical Committees 153 19.4 ODaF (Open Data Foundation) 154 19.5 Open Data Center Alliance 154 19.6 CSA (Cloud Security Alliance) – Big Data Working Group 155 19.7 ITU (International Telecommunications Union) 156 19.8 ISO (International Organization for Standardization) and Others 156 20 Chapter 20: Market Analysis & Forecasts 157 20.1 Global Outlook of the Big Data Market 157 20.2 Submarket Segmentation 158 20.2.1 Storage and Compute Infrastructure 159 20.2.2 Networking Infrastructure 160 20.2.3 Hadoop & Infrastructure Software 161 20.2.4 SQL 162 20.2.5 NoSQL 163 20.2.6 Analytic Platforms & Applications 164 20.2.7 Cloud Platforms 165 20.2.8 Professional Services 166 20.3 Vertical Market Segmentation 167 20.3.1 Automotive, Aerospace & Transportation 168 20.3.2 Banking & Securities 169 20.3.3 Defense & Intelligence 170 20.3.4 Education 171 20.3.5 Healthcare & Pharmaceutical 172 20.3.6 Smart Cities & Intelligent Buildings 173 20.3.7 Insurance 174 20.3.8 Manufacturing & Natural Resources 175 20.3.9 Media & Entertainment 176 20.3.10 Public Safety & Homeland Security 177 20.3.11 Public Services 178 20.3.12 Retail, Wholesale & Hospitality 179 20.3.13 Telecommunications 180 20.3.14 Utilities & Energy 181 20.3.15 Other Sectors 182 20.4 Regional Outlook 183 20.5 Asia Pacific 184 20.5.1 Country Level Segmentation 184 20.5.2 Australia 185 20.5.3 China 185 20.5.4 India 186 20.5.5 Indonesia 186 20.5.6 Japan 187 20.5.7 Malaysia 187 20.5.8 Pakistan 188 20.5.9 Philippines 188 20.5.10 Singapore 189 20.5.11 South Korea 189 20.5.12 Taiwan 190 20.5.13 Thailand 190 20.5.14 Rest of Asia Pacific 191 20.6 Eastern Europe 192 20.6.1 Country Level Segmentation 192 20.6.2 Czech Republic 193 20.6.3 Poland 193 20.6.4 Russia 194 20.6.5 Rest of Eastern Europe 194 20.7 Latin & Central America 195 20.7.1 Country Level Segmentation 195 20.7.2 Argentina 196 20.7.3 Brazil 196 20.7.4 Mexico 197 20.7.5 Rest of Latin & Central America 197 20.8 Middle East & Africa 198 20.8.1 Country Level Segmentation 198 20.8.2 Israel 199 20.8.3 Qatar 199 20.8.4 Saudi Arabia 200 20.8.5 South Africa 200 20.8.6 UAE 201 20.8.7 Rest of the Middle East & Africa 201 20.9 North America 202 20.9.1 Country Level Segmentation 202 20.9.2 Canada 203 20.9.3 USA 203 20.10 Western Europe 204 20.10.1 Country Level Segmentation 204 20.10.2 Denmark 205 20.10.3 Finland 205 20.10.4 France 206 20.10.5 Germany 206 20.10.6 Italy 207 20.10.7 Netherlands 207 20.10.8 Norway 208 20.10.9 Spain 208 20.10.10 Sweden 209 20.10.11 UK 209 20.10.12 Rest of Western Europe 210 21 Chapter 21: Vendor Landscape 211 21.1 1010data 211 21.2 Accenture 213 21.3 Actian Corporation 215 21.4 Actuate Corporation 217 21.5 Adaptive Insights 219 21.6 Advizor Solutions 220 21.7 AeroSpike 221 21.8 AFS Technologies 223 21.9 Alpine Data Labs 224 21.10 Alteryx 225 21.11 Altiscale 227 21.12 Antivia 228 21.13 Arcplan 229 21.14 Attivio 230 21.15 Automated Insights 232 21.16 AWS (Amazon Web Services) 233 21.17 Ayasdi 235 21.18 Basho 236 21.19 BeyondCore 238 21.20 Birst 239 21.21 Bitam 240 21.22 Board International 241 21.23 Booz Allen Hamilton 242 21.24 Capgemini 244 21.25 Cellwize 246 21.26 Centrifuge Systems 247 21.27 CenturyLink 248 21.28 Chartio 249 21.29 Cisco Systems 250 21.30 ClearStory Data 252 21.31 Cloudera 253 21.32 Comptel 255 21.33 Concurrent 257 21.34 Contexti 258 21.35 Couchbase 259 21.36 CSC (Computer Science Corporation) 261 21.37 DataHero 262 21.38 Datameer 263 21.39 DataRPM 264 21.40 DataStax 265 21.41 Datawatch Corporation 266 21.42 DDN (DataDirect Network) 267 21.43 Decisyon 268 21.44 Dell 269 21.45 Deloitte 271 21.46 Denodo Technologies 272 21.47 Digital Reasoning 273 21.48 Dimensional Insight 274 21.49 Domo 275 21.50 Dundas Data Visualization 276 21.51 Eligotech 277 21.52 EMC Corporation 278 21.53 Engineering Group (Engineering Ingegneria Informatica) 279 21.54 eQ Technologic 280 21.55 Facebook 281 21.56 FICO 283 21.57 Fractal Analytics 284 21.58 Fujitsu 285 21.59 Fusion-io 287 21.60 GE (General Electric) 288 21.61 GoodData Corporation 289 21.62 Google 290 21.63 Guavus 291 21.64 HDS (Hitachi Data Systems) 292 21.65 Hortonworks 293 21.66 HPE (Hewlett Packard Enterprise) 294 21.67 IBM 295 21.68 iDashboards 296 21.69 Incorta 297 21.70 InetSoft Technology Corporation 298 21.71 InfiniDB 299 21.72 Infor 301 21.73 Informatica Corporation 302 21.74 Information Builders 303 21.75 Intel 304 21.76 Jedox 305 21.77 Jinfonet Software 306 21.78 Juniper Networks 307 21.79 Knime 308 21.80 Kofax 309 21.81 Kognitio 310 21.82 L-3 Communications 311 21.83 Lavastorm Analytics 312 21.84 Logi Analytics 313 21.85 Looker Data Sciences 314 21.86 LucidWorks 315 21.87 Maana 316 21.88 Manthan Software Services 317 21.89 MapR 318 21.90 MarkLogic 319 21.91 MemSQL 320 21.92 Microsoft 321 21.93 MicroStrategy 323 21.94 MongoDB (formerly 10gen) 324 21.95 Mu Sigma 325 21.96 NTT Data 326 21.97 Neo Technology 327 21.98 NetApp 328 21.99 Nutonian 329 21.100 OpenText Corporation 330 21.101 Opera Solutions 331 21.102 Oracle 332 21.103 Palantir Technologies 333 21.104 Panorama Software 334 21.105 ParStream 335 21.106 Pentaho 336 21.107 Phocas 337 21.108 Pivotal Software 338 21.109 Platfora 339 21.110 Prognoz 340 21.111 PwC 341 21.112 Pyramid Analytics 342 21.113 Qlik 343 21.114 Quantum Corporation 344 21.115 Qubole 345 21.116 Rackspace 346 21.117 RapidMiner 347 21.118 Recorded Future 348 21.119 RJMetrics 349 21.120 Salesforce.com 350 21.121 Sailthru 351 21.122 Salient Management Company 352 21.123 SAP 353 21.124 SAS Institute 354 21.125 SGI 355 21.126 SiSense 356 21.127 Software AG 357 21.128 Splice Machine 358 21.129 Splunk 359 21.130 Sqrrl 360 21.131 Strategy Companion 361 21.132 Supermicro 362 21.133 Syncsort 363 21.134 SynerScope 364 21.135 Tableau Software 365 21.136 Talend 366 21.137 Targit 367 21.138 TCS (Tata Consultancy Services) 368 21.139 Teradata 369 21.140 Think Big Analytics 370 21.141 ThoughtSpot 371 21.142 TIBCO Software 372 21.143 Tidemark 373 21.144 VMware (EMC Subsidiary) 374 21.145 WiPro 375 21.146 Yellowfin International 376 21.147 Zendesk 377 21.148 Zettics 378 21.149 Zoomdata 379 21.150 Zucchetti 380 22 Chapter 22: Conclusion & Strategic Recommendations 381 22.1 Big Data Technology: Beyond Data Capture & Analytics 381 22.2 Transforming IT from a Cost Center to a Profit Center 381 22.3 Can Privacy Implications Hinder Success? 382 22.4 Will Regulation have a Negative Impact on Big Data Investments? 382 22.5 Battling Organization & Data Silos 383 22.6 Software vs. Hardware Investments 384 22.7 Vendor Share: Who Leads the Market? 385 22.8 Big Data Driving Wider IT Industry Investments 386 22.9 Assessing the Impact of IoT & M2M 387 22.10 Recommendations 388 22.10.1 Big Data Hardware, Software & Professional Services Providers 388 22.10.2 Enterprises 389
List of Figures Figure 1: Reactive vs. Proactive Analytics 48 Figure 2: Big Data Industry Roadmap 145 Figure 3: The Big Data Value Chain 148 Figure 4: Global Big Data Revenue: 2016 - 2030 ($ Million) 158 Figure 5: Global Big Data Revenue by Submarket: 2016 - 2030 ($ Million) 159 Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2016 - 2030 ($ Million) 160 Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2016 - 2030 ($ Million) 161 Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2016 - 2030 ($ Million) 162 Figure 9: Global Big Data SQL Submarket Revenue: 2016 - 2030 ($ Million) 163 Figure 10: Global Big Data NoSQL Submarket Revenue: 2016 - 2030 ($ Million) 164 Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2016 - 2030 ($ Million) 165 Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2016 - 2030 ($ Million) 166 Figure 13: Global Big Data Professional Services Submarket Revenue: 2016 - 2030 ($ Million) 167 Figure 14: Global Big Data Revenue by Vertical Market: 2016 - 2030 ($ Million) 168 Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2016 - 2030 ($ Million) 169 Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2016 - 2030 ($ Million) 170 Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2016 - 2030 ($ Million) 171 Figure 18: Global Big Data Revenue in the Education Sector: 2016 - 2030 ($ Million) 172 Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2016 - 2030 ($ Million) 173 Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2016 - 2030 ($ Million) 174 Figure 21: Global Big Data Revenue in the Insurance Sector: 2016 - 2030 ($ Million) 175 Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2016 - 2030 ($ Million) 176 Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2016 - 2030 ($ Million) 177 Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2016 - 2030 ($ Million) 178 Figure 25: Global Big Data Revenue in the Public Services Sector: 2016 - 2030 ($ Million) 179 Figure 26: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2016 - 2030 ($ Million) 180 Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2016 - 2030 ($ Million) 181 Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2016 - 2030 ($ Million) 182 Figure 29: Global Big Data Revenue in Other Vertical Sectors: 2016 - 2030 ($ Million) 183 Figure 30: Big Data Revenue by Region: 2016 - 2030 ($ Million) 184 Figure 31: Asia Pacific Big Data Revenue: 2016 - 2030 ($ Million) 185 Figure 32: Asia Pacific Big Data Revenue by Country: 2016 - 2030 ($ Million) 185 Figure 33: Australia Big Data Revenue: 2016 - 2030 ($ Million) 186 Figure 34: China Big Data Revenue: 2016 - 2030 ($ Million) 186 Figure 35: India Big Data Revenue: 2016 - 2030 ($ Million) 187 Figure 36: Indonesia Big Data Revenue: 2016 - 2030 ($ Million) 187 Figure 37: Japan Big Data Revenue: 2016 - 2030 ($ Million) 188 Figure 38: Malaysia Big Data Revenue: 2016 - 2030 ($ Million) 188 Figure 39: Pakistan Big Data Revenue: 2016 - 2030 ($ Million) 189 Figure 40: Philippines Big Data Revenue: 2016 - 2030 ($ Million) 189 Figure 41: Singapore Big Data Revenue: 2016 - 2030 ($ Million) 190 Figure 42: South Korea Big Data Revenue: 2016 - 2030 ($ Million) 190 Figure 43: Taiwan Big Data Revenue: 2016 - 2030 ($ Million) 191 Figure 44: Thailand Big Data Revenue: 2016 - 2030 ($ Million) 191 Figure 45: Big Data Revenue in the Rest of Asia Pacific: 2016 - 2030 ($ Million) 192 Figure 46: Eastern Europe Big Data Revenue: 2016 - 2030 ($ Million) 193 Figure 47: Eastern Europe Big Data Revenue by Country: 2016 - 2030 ($ Million) 193 Figure 48: Czech Republic Big Data Revenue: 2016 - 2030 ($ Million) 194 Figure 49: Poland Big Data Revenue: 2016 - 2030 ($ Million) 194 Figure 50: Russia Big Data Revenue: 2016 - 2030 ($ Million) 195 Figure 51: Big Data Revenue in the Rest of Eastern Europe: 2016 - 2030 ($ Million) 195 Figure 52: Latin & Central America Big Data Revenue: 2016 - 2030 ($ Million) 196 Figure 53: Latin & Central America Big Data Revenue by Country: 2016 - 2030 ($ Million) 196 Figure 54: Argentina Big Data Revenue: 2016 - 2030 ($ Million) 197 Figure 55: Brazil Big Data Revenue: 2016 - 2030 ($ Million) 197 Figure 56: Mexico Big Data Revenue: 2016 - 2030 ($ Million) 198 Figure 57: Big Data Revenue in the Rest of Latin & Central America: 2016 - 2030 ($ Million) 198 Figure 58: Middle East & Africa Big Data Revenue: 2016 - 2030 ($ Million) 199 Figure 59: Middle East & Africa Big Data Revenue by Country: 2016 - 2030 ($ Million) 199 Figure 60: Israel Big Data Revenue: 2016 - 2030 ($ Million) 200 Figure 61: Qatar Big Data Revenue: 2016 - 2030 ($ Million) 200 Figure 62: Saudi Arabia Big Data Revenue: 2016 - 2030 ($ Million) 201 Figure 63: South Africa Big Data Revenue: 2016 - 2030 ($ Million) 201 Figure 64: UAE Big Data Revenue: 2016 - 2030 ($ Million) 202 Figure 65: Big Data Revenue in the Rest of the Middle East & Africa: 2016 - 2030 ($ Million) 202 Figure 66: North America Big Data Revenue: 2016 - 2030 ($ Million) 203 Figure 67: North America Big Data Revenue by Country: 2016 - 2030 ($ Million) 203 Figure 68: Canada Big Data Revenue: 2016 - 2030 ($ Million) 204 Figure 69: USA Big Data Revenue: 2016 - 2030 ($ Million) 204 Figure 70: Western Europe Big Data Revenue: 2016 - 2030 ($ Million) 205 Figure 71: Western Europe Big Data Revenue by Country: 2016 - 2030 ($ Million) 205 Figure 72: Denmark Big Data Revenue: 2016 - 2030 ($ Million) 206 Figure 73: Finland Big Data Revenue: 2016 - 2030 ($ Million) 206 Figure 74: France Big Data Revenue: 2016 - 2030 ($ Million) 207 Figure 75: Germany Big Data Revenue: 2016 - 2030 ($ Million) 207 Figure 76: Italy Big Data Revenue: 2016 - 2030 ($ Million) 208 Figure 77: Netherlands Big Data Revenue: 2016 - 2030 ($ Million) 208 Figure 78: Norway Big Data Revenue: 2016 - 2030 ($ Million) 209 Figure 79: Spain Big Data Revenue: 2016 - 2030 ($ Million) 209 Figure 80: Sweden Big Data Revenue: 2016 - 2030 ($ Million) 210 Figure 81: UK Big Data Revenue: 2016 - 2030 ($ Million) 210 Figure 82: Big Data Revenue in the Rest of Western Europe: 2016 - 2030 ($ Million) 211 Figure 83: Global Big Data Revenue by Hardware, Software & Professional Services: 2016 – 2030 ($ Million) 385 Figure 84: Big Data Vendor Market Share (%) 386 Figure 85: Global IT Expenditure Driven by Big Data Investments: 2016 - 2030 ($ Million) 387 Figure 86: Global M2M Connections by Access Technology: 2016 – 2030 (Millions) 388
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