Date: Tue, 2 May 2017 00:37:07 -0700 From: "Andy Silva" <andy.silva@snsmarketreports.com> To: freebsd-ppc@freebsd.org Subject: The Big Data Market: 2017 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts (Report) Message-ID: <2164405115656316561775@Ankur>
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The Big Data Market: 2017 =96 2030 =96 Opportunities, Challenges, Strategie= s, Industry Verticals & Forecasts (Report) Hello Please find the latest SNS Research report summary to you and your team, " = The Big Data Market: 2017 =96 2030 =96 Opportunities, Challenges, Strategie= s, Industry 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. Despite challenges relating to privacy concer= ns and organizational resistance, Big Data investments continue to gain mom= entum throughout the globe. SNS Research estimates that Big Data investment= s will account for over $57 Billion in 2017 alone. These investments are fu= rther expected to grow at a CAGR of approximately 10% over the next three y= ears. Report Information: Release Date: April 2017 Number of Pages: 498 Number of Tables and Figures: 89 Key Questions Answered: 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 2017, Big Data vendors will pocket over $57 Billion from hardware, softw= are and professional services revenues. These investments are further expec= ted to grow at a CAGR of approximately 10% over the next four years, eventu= ally 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 domest= ic industries. By the end of 2017, SNS Research estimates that as much as 30% of all Big D= ata workloads will be processed via cloud services as enterprises seek to a= void large-scale infrastructure investments and security issues associated = with on-premise implementations. The vendor arena is continuing to consolidate with several prominent M&A de= als such as computer hardware giant Dell's $60 Billion merger with data sto= rage specialist EMC. 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 over 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 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 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: 2017 =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 Chapter 1: Introduction 1.1 Executive Summary 1.2 Topics Covered 1.3 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 Chapter 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 Chapter 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 Chapter 4: Big Data in Automotive, Aerospace & Transportation 4.1 Overview & Investment Potential 4.2 Key Applications 4.2.1 Autonomous Driving 4.2.2 Warranty Analytics for Automotive OEMs 4.2.3 Predictive Aircraft Maintenance & Fuel Optimization 4.2.4 Air Traffic Control 4.2.5 Transport Fleet Optimization 4.2.6 UBI (Usage Based Insurance) 4.3 Case Studies 4.3.1 Delphi Automotive: Monetizing Connected Vehicles with Big Data 4.3.2 Boeing: Making Flying More Efficient with Big Data 4.3.3 BMW: Eliminating Defects in New Vehicle Models with Big Data 4.3.4 Toyota Motor Corporation: Powering Smart Cars with Big Data 4.3.5 Ford Motor Company: Making Efficient Transportation Decisions with Bi= g Data =20 5 Chapter 5: Big Data in Banking & Securities 5.1 Overview & Investment Potential 5.2 Key Applications 5.2.1 Customer Retention & Personalized Product Offering 5.2.2 Risk Management 5.2.3 Fraud Detection 5.2.4 Credit Scoring 5.3 Case Studies 5.3.1 HSBC Group: Avoiding Regulatory Penalties with Big Data 5.3.2 JPMorgan Chase & Co.: Improving Business Processes with Big Data 5.3.3 OTP Bank: Reducing Loan Defaults with Big Data 5.3.4 CBA (Commonwealth Bank of Australia): Providing Personalized Services= with Big Data =20 6 Chapter 6: Big Data in Defense & Intelligence 6.1 Overview & Investment Potential 6.2 Key Applications 6.2.1 Intelligence Gathering 6.2.2 Battlefield Analytics 6.2.3 Energy Saving Opportunities in the Battlefield 6.2.4 Preventing Injuries on the Battlefield 6.3 Case Studies 6.3.1 U.S. Air Force: Providing Actionable Intelligence to Warfighters with= Big Data 6.3.2 Royal Navy: Empowering Submarine Warfare with Big Data 6.3.3 NSA (National Security Agency): Capitalizing on Big Data to Detect Th= reats 6.3.4 Ministry of State Security, China: Predictive Policing with Big Data 6.3.5 French DGSE (General Directorate for External Security): Enhancing In= telligence with Big Data =20 7 Chapter 7: Big Data in Education 7.1 Overview & Investment Potential 7.2 Key Applications 7.2.1 Information Integration 7.2.2 Identifying Learning Patterns 7.2.3 Enabling Student-Directed Learning 7.3 Case Studies 7.3.1 Purdue University: Ensuring Successful Higher Education Outcomes with= Big Data 7.3.2 Nottingham Trent University: Successful Student Outcomes with Big Data 7.3.3 Edith Cowen University: Increasing Student Retention with Big Data =20 8 Chapter 8: Big Data in Healthcare & Pharma 8.1 Overview & Investment Potential 8.2 Key Applications 8.2.1 Managing Population Health Efficiently 8.2.2 Improving Patient Care with Medical Data Analytics 8.2.3 Improving Clinical Development & Trials 8.2.4 Drug Development: Improving Time to Market 8.3 Case Studies 8.3.1 Amino: Healthcare Transparency with Big Data 8.3.2 Novartis: Digitizing Healthcare with Big Data 8.3.3 GSK (GlaxoSmithKline): Accelerating Drug Discovering with Big Data 8.3.4 Pfizer: Developing Effective and Targeted Therapies with Big Data 8.3.5 Roche: Personalizing Healthcare with Big Data 8.3.6 Sanofi: Proactive Diabetes Care with Big Data =20 9 Chapter 9: Big Data in Smart Cities & Intelligent Buildings 9.1 Overview & Investment Potential 9.2 Key Applications 9.2.1 Energy Optimization & Fault Detection 9.2.2 Intelligent Building Analytics 9.2.3 Urban Transportation Management 9.2.4 Optimizing Energy Production 9.2.5 Water Management 9.2.6 Urban Waste Management 9.3 Case Studies 9.3.1 Singapore: Building a Smart Nation with Big Data 9.3.2 Glasgow City Council: Promoting Smart City Efforts with Big Data 9.3.3 OVG Real Estate: Powering the World=92s Most Intelligent Building wit= h Big Data =20 10 Chapter 10: Big Data in Insurance 10.1 Overview & Investment Potential 10.2 Key Applications 10.2.1 Claims Fraud Mitigation 10.2.2 Customer Retention & Profiling 10.2.3 Risk Management 10.3 Case Studies 10.3.1 Zurich Insurance Group: Enhancing Risk Management with Big Data 10.3.2 RSA Group: Improving Customer Relations with Big Data 10.3.3 Primerica: Improving Insurance Sales Force Productivity with Big Data =20 11 Chapter 11: Big Data in Manufacturing & Natural Resources 11.1 Overview & Investment Potential 11.2 Key Applications 11.2.1 Asset Maintenance & Downtime Reduction 11.2.2 Quality & Environmental Impact Control 11.2.3 Optimized Supply Chain 11.2.4 Exploration & Identification of Natural Resources 11.3 Case Studies 11.3.1 Intel Corporation: Cutting Manufacturing Costs with Big Data 11.3.2 Dow Chemical Company: Optimizing Chemical Manufacturing with Big Data 11.3.3 Michelin: Improving the Efficiency of Supply Chain and Manufacturing= with Big Data 11.3.4 Brunei: Saving Natural Resources with Big Data =20 12 Chapter 12: Big Data in Web, Media & Entertainment 12.1 Overview & Investment Potential 12.2 Key Applications 12.2.1 Audience & Advertising Optimization 12.2.2 Channel Optimization 12.2.3 Recommendation Engines 12.2.4 Optimized Search 12.2.5 Live Sports Event Analytics 12.2.6 Outsourcing Big Data Analytics to Other Verticals 12.3 Case Studies 12.3.1 Netflix: Improving Viewership with Big Data 12.3.2 NFL (National Football League): Improving Stadium Experience with Bi= g Data 12.3.3 Walt Disney Company: Enhancing Theme Park Experience with Big Data 12.3.4 Baidu: Reshaping Search Capabilities with Big Data 12.3.5 Constant Contact: Effective Marketing with Big Data =20 13 Chapter 13: Big Data in Public Safety & Homeland Security 13.1 Overview & Investment Potential 13.2 Key Applications 13.2.1 Cyber Crime Mitigation 13.2.2 Crime Prediction Analytics 13.2.3 Video Analytics & Situational Awareness 13.3 Case Studies 13.3.1 DHS (U.S. 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 Chapter 14: Big Data in Public Services 14.1 Overview & Investment Potential 14.2 Key Applications 14.2.1 Public Sentiment Analysis 14.2.2 Tax Collection & Fraud Detection 14.2.3 Economic Analysis 14.2.4 Predicting & Mitigating Disasters 14.3 Case Studies 14.3.1 ONS (Office for National Statistics): Exploring the UK Economy with = Big Data 14.3.2 New York State Department of Taxation and Finance: Increasing Tax Re= venue with Big Data 14.3.3 Alameda County Social Services Agency: Benefit Fraud Reduction with = Big Data 14.3.4 City of Chicago: Improving Government Productivity with Big Data 14.3.5 FDNY (Fire Department of the City of New York): Fighting Fires with = Big Data 14.3.6 Ambulance Victoria: Improving Patient Survival Rates with Big Data =20 15 Chapter 15: Big Data in Retail, Wholesale & Hospitality 15.1 Overview & Investment Potential 15.2 Key Applications 15.2.1 Customer Sentiment Analysis 15.2.2 Customer & Branch Segmentation 15.2.3 Price Optimization 15.2.4 Personalized Marketing 15.2.5 Optimizing & Monitoring the Supply Chain 15.2.6 In-Field Sales Analytics 15.3 Case Studies 15.3.1 Walmart: Making Smarter Stocking Decision with Big Data 15.3.2 Tesco: Reducing Supermarket Energy Bills with Big Data 15.3.3 Marriott International: Elevating Guest Services with Big Data 15.3.4 JJ Food Service: Predictive Wholesale Shopping Lists with Big Data =20 16 Chapter 16: Big Data in Telecommunications 16.1 Overview & Investment Potential 16.2 Key Applications 16.2.1 Network Performance & Coverage Optimization 16.2.2 Customer Churn Prevention 16.2.3 Personalized Marketing 16.2.4 Tailored Location Based Services 16.2.5 Fraud Detection 16.3 Case Studies 16.3.1 BT Group: Hunting Down Nuisance Callers with Big Data 16.3.2 AT&T: Smart Network Management with Big Data 16.3.3 T-Mobile USA: Cutting Down Churn Rate with Big Data 16.3.4 TEOCO: Helping Service Providers Save Millions with Big Data 16.3.5 Freedom Mobile: Optimizing Video Quality with Big Data 16.3.6 Coriant: SaaS Based Analytics with Big Data =20 17 Chapter 17: Big Data in Utilities & Energy 17.1 Overview & Investment Potential 17.2 Key Applications 17.2.1 Customer Retention 17.2.2 Forecasting Energy 17.2.3 Billing Analytics 17.2.4 Predictive Maintenance 17.2.5 Maximizing the Potential of Drilling 17.2.6 Production Optimization 17.3 Case Studies 17.3.1 Royal Dutch Shell: Developing Data-Driven Oil Fields with Big Data 17.3.2 British Gas: Improving Customer Service with Big Data 17.3.3 Oncor Electric Delivery: Intelligent Power Grid Management with Big = Data =20 18 Chapter 18: Big Data Industry Roadmap & Value Chain 18.1 Big Data Industry Roadmap 18.1.1 2017 =96 2020: Investments in Predictive Analytics & SaaS-Based Big = Data Offerings 18.1.2 2020 =96 2025: Growing Focus on Cognitive & Personalized Analytics 18.1.3 2025 =96 2030: Convergence with Future IoT Applications 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 Chapter 19: Standardization & Regulatory Initiatives 19.1 ASF (Apache Software Foundation) 19.1.1 Management of Hadoop 19.1.2 Big Data Projects Beyond Hadoop 19.2 CSA (Cloud Security Alliance) 19.2.1 BDWG (Big Data Working Group) 19.3 CSCC (Cloud Standards Customer Council) 19.3.1 Big Data Working Group 19.4 DMG (Data Mining Group) 19.4.1 PMML (Predictive Model Markup Language) Working Group 19.4.2 PFA (Portable Format for Analytics) Working Group 19.5 IEEE (Institute of Electrical and Electronics Engineers) =96Big Data I= nitiative 19.6 INCITS (InterNational Committee for Information Technology Standards) 19.6.1 Big Data Technical Committee 19.7 ISO (International Organization for Standardization) 19.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 19.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 19.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 19.7.4 ISO/IEC JTC 1/WG 9: Big Data 19.7.5 Collaborations with Other ISO Work Groups 19.8 ITU (International Telecommunications Union) 19.8.1 ITU-T Y.3600: Big Data =96 Cloud Computing Based Requirements and Ca= pabilities 19.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 19.8.3 Other Relevant Work 19.9 Linux Foundation 19.9.1 ODPi (Open Ecosystem of Big Data) 19.10 NIST (National Institute of Standards and Technology) 19.10.1 NBD-PWG (NIST Big Data Public Working Group) 19.11 OASIS (Organization for the Advancement of Structured Information Sta= ndards) 19.11.1 Technical Committees 19.12 ODaF (Open Data Foundation) 19.12.1 Big Data Accessibility 19.13 ODCA (Open Data Center Alliance) 19.13.1 Work on Big Data 19.14 OGC (Open Geospatial Consortium) 19.14.1 Big Data DWG (Domain Working Group) 19.15 TM Forum 19.15.1 Big Data Analytics Strategic Program 19.16 TPC (Transaction Processing Performance Council) 19.16.1 TPC-BDWG (TPC Big Data Working Group) 19.17 W3C (World Wide Web Consortium) 19.17.1 Big Data Community Group 19.17.2 Open Government Community Group =20 20 Chapter 20: Market Analysis & Forecasts 20.1 Global Outlook for 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 Chapter 21: Vendor Landscape 21.1 1010data 21.2 Absolutdata 21.3 Accenture 21.4 Actian Corporation 21.5 Adaptive Insights 21.6 Advizor Solutions 21.7 AeroSpike 21.8 AFS Technologies 21.9 Alation 21.10 Algorithmia 21.11 Alluxio 21.12 Alpine Data 21.13 Alteryx 21.14 AMD (Advanced Micro Devices) 21.15 Apixio 21.16 Arcadia Data 21.17 Arimo 21.18 ARM 21.19 AtScale 21.20 Attivio 21.21 Attunity 21.22 Automated Insights 21.23 AWS (Amazon Web Services) 21.24 Axiomatics 21.25 Ayasdi 21.26 Basho Technologies 21.27 BCG (Boston Consulting Group) 21.28 Bedrock Data 21.29 BetterWorks 21.30 Big Cloud Analytics 21.31 Big Panda 21.32 Birst 21.33 Bitam 21.34 Blue Medora 21.35 BlueData Software 21.36 BlueTalon 21.37 BMC Software 21.38 BOARD International 21.39 Booz Allen Hamilton 21.40 Boxever 21.41 CACI International 21.42 Cambridge Semantics 21.43 Capgemini 21.44 Cazena 21.45 Centrifuge Systems 21.46 CenturyLink 21.47 Chartio 21.48 Cisco Systems 21.49 Civis Analytics 21.50 ClearStory Data 21.51 Cloudability 21.52 Cloudera 21.53 Clustrix 21.54 CognitiveScale 21.55 Collibra 21.56 Concurrent Computer Corporation 21.57 Confluent 21.58 Contexti 21.59 Continuum Analytics 21.60 Couchbase 21.61 CrowdFlower 21.62 Databricks 21.63 DataGravity 21.64 Dataiku 21.65 Datameer 21.66 DataRobot 21.67 DataScience 21.68 DataStax 21.69 DataTorrent 21.70 Datawatch Corporation 21.71 Datos IO 21.72 DDN (DataDirect Networks) 21.73 Decisyon 21.74 Dell Technologies 21.75 Deloitte 21.76 Demandbase 21.77 Denodo Technologies 21.78 Digital Reasoning Systems 21.79 Dimensional Insight 21.80 Dolphin Enterprise Solutions Corporation 21.81 Domino Data Lab 21.82 Domo 21.83 DriveScale 21.84 Dundas Data Visualization 21.85 DXC Technology 21.86 Eligotech 21.87 Engineering Group (Engineering Ingegneria Informatica) 21.88 EnterpriseDB 21.89 eQ Technologic 21.90 Ericsson 21.91 EXASOL 21.92 Facebook 21.93 FICO (Fair Isaac Corporation) 21.94 Fractal Analytics 21.95 Fujitsu 21.96 Fuzzy Logix 21.97 Gainsight 21.98 GE (General Electric) 21.99 Glassbeam 21.100 GoodData Corporation 21.101 Google 21.102 Greenwave Systems 21.103 GridGain Systems 21.104 Guavus 21.105 H2O.ai 21.106 HDS (Hitachi Data Systems) 21.107 Hedvig 21.108 Hortonworks 21.109 HPE (Hewlett Packard Enterprise) 21.110 Huawei 21.111 IBM Corporation 21.112 iDashboards 21.113 Impetus Technologies 21.114 Incorta 21.115 InetSoft Technology Corporation 21.116 Infer 21.117 Infor 21.118 Informatica Corporation 21.119 Information Builders 21.120 Infosys 21.121 Infoworks 21.122 Insightsoftware.com 21.123 InsightSquared 21.124 Intel Corporation 21.125 Interana 21.126 InterSystems Corporation 21.127 Jedox 21.128 Jethro 21.129 Jinfonet Software 21.130 Juniper Networks 21.131 KALEAO 21.132 Keen IO 21.133 Kinetica 21.134 KNIME 21.135 Kognitio 21.136 Kyvos Insights 21.137 Lavastorm 21.138 Lexalytics 21.139 Lexmark International 21.140 Logi Analytics 21.141 Longview Solutions 21.142 Looker Data Sciences 21.143 LucidWorks 21.144 Luminoso Technologies 21.145 Maana 21.146 Magento Commerce 21.147 Manthan Software Services 21.148 MapD Technologies 21.149 MapR Technologies 21.150 MariaDB Corporation 21.151 MarkLogic Corporation 21.152 Mathworks 21.153 MemSQL 21.154 Metric Insights 21.155 Microsoft Corporation 21.156 MicroStrategy 21.157 Minitab 21.158 MongoDB 21.159 Mu Sigma 21.160 Neo Technology 21.161 NetApp 21.162 Nimbix 21.163 Nokia 21.164 NTT Data Corporation 21.165 Numerify 21.166 NuoDB 21.167 Nutonian 21.168 NVIDIA Corporation 21.169 Oblong Industries 21.170 OpenText Corporation 21.171 Opera Solutions 21.172 Optimal Plus 21.173 Oracle Corporation 21.174 Palantir Technologies 21.175 Panorama Software 21.176 Paxata 21.177 Pentaho Corporation 21.178 Pepperdata 21.179 Phocas Software 21.180 Pivotal Software 21.181 Prognoz 21.182 Progress Software Corporation 21.183 PwC (PricewaterhouseCoopers International) 21.184 Pyramid Analytics 21.185 Qlik 21.186 Quantum Corporation 21.187 Qubole 21.188 Rackspace 21.189 Radius Intelligence 21.190 RapidMiner 21.191 Recorded Future 21.192 Red Hat 21.193 Redis Labs 21.194 RedPoint Global 21.195 Reltio 21.196 RStudio 21.197 Ryft Systems 21.198 Sailthru 21.199 Salesforce.com 21.200 Salient Management Company 21.201 Samsung Group 21.202 SAP 21.203 SAS Institute 21.204 ScaleDB 21.205 ScaleOut Software 21.206 SCIO Health Analytics 21.207 Seagate Technology 21.208 Sinequa 21.209 SiSense 21.210 SnapLogic 21.211 Snowflake Computing 21.212 Software AG 21.213 Splice Machine 21.214 Splunk 21.215 Sqrrl 21.216 Strategy Companion Corporation 21.217 StreamSets 21.218 Striim 21.219 Sumo Logic 21.220 Supermicro (Super Micro Computer) 21.221 Syncsort 21.222 SynerScope 21.223 Tableau Software 21.224 Talena 21.225 Talend 21.226 Tamr 21.227 TARGIT 21.228 TCS (Tata Consultancy Services) 21.229 Teradata Corporation 21.230 ThoughtSpot 21.231 TIBCO Software 21.232 Tidemark 21.233 Toshiba Corporation 21.234 Trifacta 21.235 Unravel Data 21.236 VMware 21.237 VoltDB 21.238 Waterline Data 21.239 Western Digital Corporation 21.240 WiPro 21.241 Workday 21.242 Xplenty 21.243 Yellowfin International 21.244 Yseop 21.245 Zendesk 21.246 Zoomdata 21.247 Zucchetti =20 22 Chapter 22: Conclusion & Strategic Recommendations 22.1 Big Data Technology: Beyond Data Capture & Analytics 22.2 Transforming IT from a Cost Center to a Profit Center 22.3 Can Privacy Implications Hinder Success=3F 22.4 Maximizing Innovation with Careful Regulation 22.5 Battling Organizational & Data Silos 22.6 Moving Big Data to the Cloud 22.7 Software vs. Hardware Investments 22.8 Vendor Share: Who Leads the Market=3F 22.9 Moving Towards Consolidation: Review of M&A Activity in the Vendor Are= na 22.10 Big Data Driving Wider IT Industry Investments 22.11 Assessing the Impact of IoT & M2M 22.12 Recommendations 22.12.1 Big Data Hardware, Software & Professional Services Providers 22.12.2 Enterprises =20 List of Figures: =20 Figure 1: Hadoop Architecture Figure 2: Reactive vs. Proactive Analytics Figure 3: Big Data Industry Roadmap Figure 4: Big Data Value Chain Figure 5: Key Aspects of Big Data Standardization Figure 6: Global Big Data Revenue: 2017 - 2030 ($ Million) Figure 7: Global Big Data Revenue by Submarket: 2017 - 2030 ($ Million) Figure 8: Global Big Data Storage and Compute Infrastructure Submarket Reve= nue: 2017 - 2030 ($ Million) Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2017= - 2030 ($ Million) Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Reven= ue: 2017 - 2030 ($ Million) Figure 11: Global Big Data SQL Submarket Revenue: 2017 - 2030 ($ Million) Figure 12: Global Big Data NoSQL Submarket Revenue: 2017 - 2030 ($ Million) Figure 13: Global Big Data Analytic Platforms & Applications Submarket Reve= nue: 2017 - 2030 ($ Million) Figure 14: Global Big Data Cloud Platforms Submarket Revenue: 2017 - 2030 (= $ Million) Figure 15: Global Big Data Professional Services Submarket Revenue: 2017 - = 2030 ($ Million) Figure 16: Global Big Data Revenue by Vertical Market: 2017 - 2030 ($ Milli= on) Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transport= ation Sector: 2017 - 2030 ($ Million) Figure 18: Global Big Data Revenue in the Banking & Securities Sector: 2017= - 2030 ($ Million) Figure 19: Global Big Data Revenue in the Defense & Intelligence Sector: 20= 17 - 2030 ($ Million) Figure 20: Global Big Data Revenue in the Education Sector: 2017 - 2030 ($ = Million) Figure 21: Global Big Data Revenue in the Healthcare & Pharmaceutical Secto= r: 2017 - 2030 ($ Million) Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildi= ngs Sector: 2017 - 2030 ($ Million) Figure 23: Global Big Data Revenue in the Insurance Sector: 2017 - 2030 ($ = Million) Figure 24: Global Big Data Revenue in the Manufacturing & Natural Resources= Sector: 2017 - 2030 ($ Million) Figure 25: Global Big Data Revenue in the Media & Entertainment Sector: 201= 7 - 2030 ($ Million) Figure 26: Global Big Data Revenue in the Public Safety & Homeland Security= Sector: 2017 - 2030 ($ Million) Figure 27: Global Big Data Revenue in the Public Services Sector: 2017 - 20= 30 ($ Million) Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality S= ector: 2017 - 2030 ($ Million) Figure 29: Global Big Data Revenue in the Telecommunications Sector: 2017 -= 2030 ($ Million) Figure 30: Global Big Data Revenue in the Utilities & Energy Sector: 2017 -= 2030 ($ Million) Figure 31: Global Big Data Revenue in Other Vertical Sectors: 2017 - 2030 (= $ Million) Figure 32: Big Data Revenue by Region: 2017 - 2030 ($ Million) Figure 33: Asia Pacific Big Data Revenue: 2017 - 2030 ($ Million) Figure 34: Asia Pacific Big Data Revenue by Country: 2017 - 2030 ($ Million) Figure 35: Australia Big Data Revenue: 2017 - 2030 ($ Million) Figure 36: China Big Data Revenue: 2017 - 2030 ($ Million) Figure 37: India Big Data Revenue: 2017 - 2030 ($ Million) Figure 38: Indonesia Big Data Revenue: 2017 - 2030 ($ Million) Figure 39: Japan Big Data Revenue: 2017 - 2030 ($ Million) Figure 40: Malaysia Big Data Revenue: 2017 - 2030 ($ Million) Figure 41: Pakistan Big Data Revenue: 2017 - 2030 ($ Million) Figure 42: Philippines Big Data Revenue: 2017 - 2030 ($ Million) Figure 43: Singapore Big Data Revenue: 2017 - 2030 ($ Million) Figure 44: South Korea Big Data Revenue: 2017 - 2030 ($ Million) Figure 45: Taiwan Big Data Revenue: 2017 - 2030 ($ Million) Figure 46: Thailand Big Data Revenue: 2017 - 2030 ($ Million) Figure 47: Big Data Revenue in the Rest of Asia Pacific: 2017 - 2030 ($ Mil= lion) Figure 48: Eastern Europe Big Data Revenue: 2017 - 2030 ($ Million) Figure 49: Eastern Europe Big Data Revenue by Country: 2017 - 2030 ($ Milli= on) Figure 50: Czech Republic Big Data Revenue: 2017 - 2030 ($ Million) Figure 51: Poland Big Data Revenue: 2017 - 2030 ($ Million) Figure 52: Russia Big Data Revenue: 2017 - 2030 ($ Million) Figure 53: Big Data Revenue in the Rest of Eastern Europe: 2017 - 2030 ($ M= illion) Figure 54: Latin & Central America Big Data Revenue: 2017 - 2030 ($ Million) Figure 55: Latin & Central America Big Data Revenue by Country: 2017 - 2030= ($ Million) Figure 56: Argentina Big Data Revenue: 2017 - 2030 ($ Million) Figure 57: Brazil Big Data Revenue: 2017 - 2030 ($ Million) Figure 58: Mexico Big Data Revenue: 2017 - 2030 ($ Million) Figure 59: Big Data Revenue in the Rest of Latin & Central America: 2017 - = 2030 ($ Million) Figure 60: Middle East & Africa Big Data Revenue: 2017 - 2030 ($ Million) Figure 61: Middle East & Africa Big Data Revenue by Country: 2017 - 2030 ($= Million) Figure 62: Israel Big Data Revenue: 2017 - 2030 ($ Million) Figure 63: Qatar Big Data Revenue: 2017 - 2030 ($ Million) Figure 64: Saudi Arabia Big Data Revenue: 2017 - 2030 ($ Million) Figure 65: South Africa Big Data Revenue: 2017 - 2030 ($ Million) Figure 66: UAE Big Data Revenue: 2017 - 2030 ($ Million) Figure 67: Big Data Revenue in the Rest of the Middle East & Africa: 2017 -= 2030 ($ Million) Figure 68: North America Big Data Revenue: 2017 - 2030 ($ Million) Figure 69: North America Big Data Revenue by Country: 2017 - 2030 ($ Millio= n) Figure 70: Canada Big Data Revenue: 2017 - 2030 ($ Million) Figure 71: USA Big Data Revenue: 2017 - 2030 ($ Million) Figure 72: Western Europe Big Data Revenue: 2017 - 2030 ($ Million) Figure 73: Western Europe Big Data Revenue by Country: 2017 - 2030 ($ Milli= on) Figure 74: Denmark Big Data Revenue: 2017 - 2030 ($ Million) Figure 75: Finland Big Data Revenue: 2017 - 2030 ($ Million) Figure 76: France Big Data Revenue: 2017 - 2030 ($ Million) Figure 77: Germany Big Data Revenue: 2017 - 2030 ($ Million) Figure 78: Italy Big Data Revenue: 2017 - 2030 ($ Million) Figure 79: Netherlands Big Data Revenue: 2017 - 2030 ($ Million) Figure 80: Norway Big Data Revenue: 2017 - 2030 ($ Million) Figure 81: Spain Big Data Revenue: 2017 - 2030 ($ Million) Figure 82: Sweden Big Data Revenue: 2017 - 2030 ($ Million) Figure 83: UK Big Data Revenue: 2017 - 2030 ($ Million) Figure 84: Big Data Revenue in the Rest of Western Europe: 2017 - 2030 ($ M= illion) Figure 85: Global Big Data Workload Distribution by Environment: 2017 - 203= 0 (%) Figure 86: Global Big Data Revenue by Hardware, Software & Professional Ser= vices: 2017 - 2030 ($ Million) Figure 87: Big Data Vendor Market Share (%) Figure 88: Global IT Expenditure Driven by Big Data Investments: 2017 - 203= 0 ($ Million) Figure 89: Global M2M Connections by Access Technology: 2017 - 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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