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Published: Apr, 2017 | Pages:
498 | Publisher: SNS Research
Industry: ICT | 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 $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. 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 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 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 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 three 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 Amazon.com 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 H2O.ai 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 Insightsoftware.com 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 Salesforce.com 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 U.S. Air Force U.S. Army U.S. Coast Guard U.S. Department of Commerce U.S. Department of Defense 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
Table of Contents 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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
List of Figures 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
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