Date: Fri, 18 Jul 2014 23:00:17 +0100 From: "Andy Silva" <andy.silva@snsreports.com> To: gnome@freebsd.org Subject: The Big Data Market: 2014 - 2020 - Opportunities, Challenges, Strategies, Industry Verticals and Forecasts (Report) Message-ID: <6828619290561284318633@Sakudhwani-PC>
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The Big Data Market: 2014 - 2020 - Opportunities, Challenges, Strategies, I= ndustry Verticals and Forecasts (Report) Report Information: Release Date: May 2014 Number of Pages: 289 Number of Tables and Figures: 86 Report Overview: =20 =93Big Data=94 originally emerged as a term to describe datasets whose size= is beyond the ability of traditional databases to capture, store, manage a= nd 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 te= chnologies that capture, store, manage and analyze large and variable colle= ctions of data to solve complex problems. Amid the proliferation of real time data from sources such as mobile device= s, web, social media, sensors, log files and transactional applications, Bi= g Data has found a host of vertical market applications, ranging from fraud= detection to R&D. Despite challenges relating to privacy concerns and organizational resistan= ce, Big Data investments continue to gain momentum throughout the globe. SN= S Research estimates that Big Data investments will account for nearly $30 = Billion in 2014 alone. These investments are further expected to grow at a = CAGR of 17% over the next 6 years. The =93Big Data Market: 2014 =96 2020 =96 Opportunities, Challenges, Strate= gies, Industry Verticals & Forecasts=94 report presents an in-depth assessm= ent of the Big Data ecosystem including key market drivers, challenges, inv= estment potential, vertical market opportunities and use cases, future road= map, value chain, case studies on Big Data analytics, vendor market share a= nd strategies.=20 The report also presents market size forecasts for Big Data hardware, softw= are and professional services from 2014 through to 2020. Historical figures= are also presented for 2010, 2011, 2012 and 2013. The forecasts are furthe= r segmented for 8 horizontal submarkets, 15 vertical markets, 6 regions and= 34 countries. The report comes with an associated Excel datasheet suite covering quantita= tive data from all numeric forecasts presented in the report. =20 Key Findings: =20 The report has the following key findings: In 2014 Big Data vendors will pocket nearly $30 Billion from hardware, soft= ware and professional services revenues Big Data investments are further expected to grow at a CAGR of nearly 17% o= ver the next 6 years, eventually accounting for $76 Billion by the end of 2= 020 The market is ripe for acquisitions of pure-play Big Data startups, as comp= etition heats up between IT incumbents Nearly every large scale IT vendor maintains a Big Data portfolio At present, hardware sales and professional services account for more than = 70% of all Big Data investments Going forward, software vendors, particularly those in the Big Data analyti= cs segment, are expected to significantly increase their stake in the Big D= ata market as it matures Topics Covered: =20 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 15 vertical markets Big Data analytics technology and case studies Big Data vendor market share Company profiles and strategies of 90 Big Data ecosystem players Strategic recommendations for Big Data hardware, software and professional = services vendors and enterprises Exclusive interview transcripts of 4 players in the Big Data ecosystem Market analysis and forecasts from 2014 till 2020 Forecast Segmentation: =20 Market forecasts and historical figures are provided for each of the follow= ing submarkets and their categories: 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=20 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 & Hospitality Telecommunications Utilities & Energy Wholesale Trade 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, Finla= nd, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mex= ico, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Si= ngapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, U= K, USA Key Questions Answered: =20 The report provides answers to the following key questions: How big is the Big Data ecosystem=3F How is the ecosystem evolving by segment and region=3F What will the market size be in 2020 and at what rate will it grow=3F What trends, challenges and barriers are influencing its growth=3F Who are the key Big Data software, hardware and services vendors and what a= re their strategies=3F How much are vertical enterprises investing in Big Data=3F What opportunities exist for Big Data analytics=3F Which countries and verticals will see the highest percentage of Big Data i= nvestments=3F Table of Contents: 1 Chapter 1: Introduction 1.1 Executive Summary 1.2 Topics Covered 1.3 Historical Revenue & Forecast Segmentation 1.4 Key Questions Answered 1.5 Key Findings 1.6 Methodology 1.7 Target Audience 1.8 Companies & Organizations Mentioned 2 Chapter 2: An Overview of Big Data 2.1 What is Big Data=3F 2.2 Approaches to Big Data Processing 2.2.1 Hadoop 2.2.2 NoSQL 2.2.3 MPAD (Massively Parallel Analytic Databases) 2.2.4 Others & Analytic Technologies 2.3 Key Characteristics of Big Data 2.3.1 Volume 2.3.2 Velocity 2.3.3 Variety 2.3.4 Value 2.4 Market Growth Drivers 2.4.1 Awareness of Benefits 2.4.2 Maturation of Big Data Platforms 2.4.3 Continued Investments by Web Giants, Governments & Enterprises 2.4.4 Growth of Data Volume, Velocity & Variety 2.4.5 Vendor Commitments & Partnerships 2.4.6 Technology Trends Lowering Entry Barriers 2.5 Market Barriers 2.5.1 Lack of Analytic Specialists 2.5.2 Uncertain Big Data Strategies 2.5.3 Organizational Resistance to Big Data Adoption 2.5.4 Technical Challenges: Scalability & Maintenance 2.5.5 Security & Privacy Concerns 3 Chapter 3: Vertical Opportunities & Use Cases for Big Data 3.1 Automotive, Aerospace & Transportation 3.1.1 Predictive Warranty Analysis 3.1.2 Predictive Aircraft Maintenance & Fuel Optimization 3.1.3 Air Traffic Control 3.1.4 Transport Fleet Optimization 3.2 Banking & Securities 3.2.1 Customer Retention & Personalized Product Offering 3.2.2 Risk Management 3.2.3 Fraud Detection 3.2.4 Credit Scoring 3.3 Defense & Intelligence 3.3.1 Intelligence Gathering 3.3.2 Energy Saving Opportunities in the Battlefield 3.3.3 Preventing Injuries on the Battlefield 3.4 Education 3.4.1 Information Integration 3.4.2 Identifying Learning Patterns 3.4.3 Enabling Student-Directed Learning 3.5 Healthcare & Pharmaceutical 3.5.1 Managing Population Health Efficiently 3.5.2 Improving Patient Care with Medical Data Analytics 3.5.3 Improving Clinical Development & Trials 3.5.4 Improving Time to Market 3.6 Smart Cities & Intelligent Buildings 3.6.1 Energy Optimization & Fault Detection 3.6.2 Intelligent Building Analytics 3.6.3 Urban Transportation Management 3.6.4 Optimizing Energy Production 3.6.5 Water Management 3.6.6 Urban Waste Management 3.7 Insurance 3.7.1 Claims Fraud Mitigation 3.7.2 Customer Retention & Profiling 3.7.3 Risk Management 3.8 Manufacturing & Natural Resources 3.8.1 Asset Maintenance & Downtime Reduction 3.8.2 Quality & Environmental Impact Control 3.8.3 Optimized Supply Chain 3.8.4 Exploration & Identification of Wells & Mines 3.8.5 Maximizing the Potential of Drilling 3.8.6 Production Optimization 3.9 Web, Media & Entertainment 3.9.1 Audience & Advertising Optimization 3.9.2 Channel Optimization 3.9.3 Recommendation Engines 3.9.4 Optimized Search 3.9.5 Live Sports Event Analytics 3.9.6 Outsourcing Big Data Analytics to Other Verticals 3.10 Public Safety & Homeland Security 3.10.1 Cyber Crime Mitigation 3.10.2 Crime Prediction Analytics 3.10.3 Video Analytics & Situational Awareness 3.11 Public Services 3.11.1 Public Sentiment Analysis 3.11.2 Fraud Detection & Prevention 3.11.3 Economic Analysis 3.12 Retail & Hospitality 3.12.1 Customer Sentiment Analysis 3.12.2 Customer & Branch Segmentation 3.12.3 Price Optimization 3.12.4 Personalized Marketing 3.12.5 Optimized Supply Chain 3.13 Telecommunications 3.13.1 Network Performance & Coverage Optimization 3.13.2 Customer Churn Prevention 3.13.3 Personalized Marketing 3.13.4 Location Based Services 3.13.5 Fraud Detection 3.14 Utilities & Energy 3.14.1 Customer Retention 3.14.2 Forecasting Energy 3.14.3 Billing Analytics 3.14.4 Predictive Maintenance 3.14.5 Turbine Placement Optimization 3.15 Wholesale Trade 3.15.1 In-field Sales Analytics 3.15.2 Monitoring the Supply Chain 4 Chapter 4: Big Data Industry Roadmap & Value Chain 4.1 Big Data Industry Roadmap 4.1.1 2010 =96 2013: Initial Hype and the Rise of Analytics 4.1.2 2014 =96 2017: Emergence of SaaS Based Big Data Solutions 4.1.3 2018 =96 2020 & Beyond: Large Scale Proliferation of Scalable Machine= Learning 4.2 The Big Data Value Chain 4.2.1 Hardware Providers 4.2.1.1 Storage & Compute Infrastructure Providers 4.2.1.2 Networking Infrastructure Providers 4.2.2 Software Providers 4.2.2.1 Hadoop & Infrastructure Software Providers 4.2.2.2 SQL & NoSQL Providers 4.2.2.3 Analytic Platform & Application Software Providers 4.2.2.4 Cloud Platform Providers 4.2.3 Professional Services Providers 4.2.4 End-to-End Solution Providers 4.2.5 Vertical Enterprises 5 Chapter 5: Big Data Analytics 5.1 What are Big Data Analytics=3F 5.2 The Importance of Analytics 5.3 Reactive vs. Proactive Analytics 5.4 Customer vs. Operational Analytics 5.5 Technology & Implementation Approaches 5.5.1 Grid Computing 5.5.2 In-Database Processing 5.5.3 In-Memory Analytics 5.5.4 Machine Learning & Data Mining 5.5.5 Predictive Analytics 5.5.6 NLP (Natural Language Processing) 5.5.7 Text Analytics 5.5.8 Visual Analytics 5.5.9 Social Media, IT & Telco Network Analytics 5.6 Vertical Market Case Studies 5.6.1 Amazon =96 Delivering Cloud Based Big Data Analytics 5.6.2 Facebook =96 Using Analytics to Monetize Users with Advertising 5.6.3 WIND Mobile =96 Using Analytics to Monitor Video Quality 5.6.4 Coriant Analytics Services =96 SaaS Based Big Data Analytics for Telc= os 5.6.5 Boeing =96 Analytics for the Battlefield 5.6.6 The Walt Disney Company =96 Utilizing Big Data and Analytics in Theme= Parks 6 Chapter 6: Standardization & Regulatory Initiatives 6.1 CSCC (Cloud Standards Customer Council) =96 Big Data Working Group 6.2 NIST (National Institute of Standards and Technology) =96 Big Data Work= ing Group 6.3 OASIS =96Technical Committees 6.4 ODaF (Open Data Foundation) 6.5 Open Data Center Alliance 6.6 CSA (Cloud Security Alliance) =96 Big Data Working Group 6.7 ITU (International Telecommunications Union) 6.8 ISO (International Organization for Standardization) and Others 7 Chapter 7: Market Analysis & Forecasts 7.1 Global Outlook of the Big Data Market 7.2 Submarket Segmentation 7.2.1 Storage and Compute Infrastructure 7.2.2 Networking Infrastructure 7.2.3 Hadoop & Infrastructure Software 7.2.4 SQL 7.2.5 NoSQL 7.2.6 Analytic Platforms & Applications 7.2.7 Cloud Platforms 7.2.8 Professional Services 7.3 Vertical Market Segmentation 7.3.1 Automotive, Aerospace & Transportation 7.3.2 Banking & Securities 7.3.3 Defense & Intelligence 7.3.4 Education 7.3.5 Healthcare & Pharmaceutical 7.3.6 Smart Cities & Intelligent Buildings 7.3.7 Insurance 7.3.8 Manufacturing & Natural Resources 7.3.9 Media & Entertainment 7.3.10 Public Safety & Homeland Security 7.3.11 Public Services 7.3.12 Retail & Hospitality 7.3.13 Telecommunications 7.3.14 Utilities & Energy 7.3.15 Wholesale Trade 7.3.16 Other Sectors 7.4 Regional Outlook 7.5 Asia Pacific 7.5.1 Country Level Segmentation 7.5.2 Australia 7.5.3 China 7.5.4 India 7.5.5 Japan 7.5.6 South Korea 7.5.7 Pakistan 7.5.8 Thailand 7.5.9 Indonesia 7.5.10 Malaysia 7.5.11 Taiwan 7.5.12 Philippines 7.5.13 Singapore 7.5.14 Rest of Asia Pacific 7.6 Eastern Europe 7.6.1 Country Level Segmentation 7.6.2 Czech Republic 7.6.3 Poland 7.6.4 Russia 7.6.5 Rest of Eastern Europe 7.7 Latin & Central America 7.7.1 Country Level Segmentation 7.7.2 Argentina 7.7.3 Brazil 7.7.4 Mexico 7.7.5 Rest of Latin & Central America 7.8 Middle East & Africa 7.8.1 Country Level Segmentation 7.8.2 South Africa 7.8.3 UAE 7.8.4 Qatar 7.8.5 Saudi Arabia 7.8.6 Israel 7.8.7 Rest of the Middle East & Africa 7.9 North America 7.9.1 Country Level Segmentation 7.9.2 USA 7.9.3 Canada 7.10 Western Europe 7.10.1 Country Level Segmentation 7.10.2 Denmark 7.10.3 Finland 7.10.4 France 7.10.5 Germany 7.10.6 Italy 7.10.7 Spain 7.10.8 Sweden 7.10.9 Norway 7.10.10 UK 7.10.11 Rest of Western Europe 8 Chapter 8: Vendor Landscape 8.1 1010data 8.2 Accenture 8.3 Actian Corporation 8.4 Actuate Corporation 8.5 AeroSpike 8.6 Alpine Data Labs 8.7 Alteryx 8.8 AWS (Amazon Web Services) 8.9 Attivio 8.10 Basho 8.11 Booz Allen Hamilton 8.12 InfiniDB 8.13 Capgemini 8.14 Cellwize 8.15 CenturyLink 8.16 Cisco Systems 8.17 Cloudera 8.18 Comptel 8.19 Contexti 8.20 Couchbase 8.21 CSC (Computer Science Corporation) 8.22 Datameer 8.23 DataStax 8.24 DDN (DataDirect Network) 8.25 Dell 8.26 Deloitte 8.27 Digital Reasoning 8.28 EMC Corporation 8.29 Facebook 8.30 Fractal Analytics 8.31 Fujitsu 8.32 Fusion-io 8.33 GE (General Electric) 8.34 GoodData Corporation 8.35 Google 8.36 Guavus 8.37 HDS (Hitachi Data Systems) 8.38 Hortonworks 8.39 HP 8.40 IBM 8.41 Informatica Corporation 8.42 Information Builders 8.43 Intel 8.44 Jaspersoft 8.45 Juniper Networks 8.46 Kognitio 8.47 Lavastorm Analytics 8.48 LucidWorks 8.49 MapR 8.50 MarkLogic 8.51 Microsoft 8.52 MicroStrategy 8.53 MongoDB (formerly 10gen) 8.54 Mu Sigma 8.55 NTT Data 8.56 Neo Technology 8.57 NetApp 8.58 Opera Solutions 8.59 Oracle 8.60 Palantir Technologies 8.61 ParStream 8.62 Pentaho 8.63 Platfora 8.64 Pivotal Software 8.65 PwC 8.66 QlikTech 8.67 Quantum Corporation 8.68 Rackspace 8.69 RainStor 8.70 Revolution Analytics 8.71 Salesforce.com 8.72 Sailthru 8.73 SAP 8.74 SAS Institute 8.75 SGI 8.76 SiSense 8.77 Software AG/Terracotta 8.78 Splunk 8.79 Sqrrl 8.80 Supermicro 8.81 Tableau Software 8.82 Talend 8.83 TCS (Tata Consultancy Services) 8.84 Teradata 8.85 Think Big Analytics 8.86 TIBCO Software 8.87 Tidemark 8.88 VMware (EMC Subsidiary) 8.89 WiPro 8.90 Zettics 9 Chapter 9: Expert Opinion =96 Interview Transcripts 9.1 Comptel 9.2 Lavastorm Analytics 9.3 ParStream 9.4 Sailthru 10 Chapter 10: Conclusion & Strategic Recommendations 10.1 Big Data Technology: Beyond Data Capture & Analytics 10.2 Transforming IT from a Cost Center to a Profit Center 10.3 Can Privacy Implications Hinder Success=3F 10.4 Will Regulation have a Negative Impact on Big Data Investments=3F 10.5 Battling Organization & Data Silos 10.6 Software vs. Hardware Investments 10.7 Vendor Share: Who Leads the Market=3F 10.8 Big Data Driving Wider IT Industry Investments 10.9 Assessing the Impact of IoT & M2M 10.10 Recommendations 10.10.1 Big Data Hardware, Software & Professional Services Providers 10.10.2 Enterprises List of Figures: Figure 1: Figure 1: Big Data Industry Roadmap Figure 2: The Big Data Value Chain Figure 3: Reactive vs. Proactive Analytics Figure 4: Global Big Data Revenue: 2010 - 2020 ($ Million) Figure 5: Global Big Data Revenue by Submarket: 2010 - 2020 ($ Million) Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Reve= nue: 2010 - 2020 ($ Million) Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2010= - 2020 ($ Million) Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenu= e: 2010 - 2020 ($ Million) Figure 9: Global Big Data SQL Submarket Revenue: 2010 - 2020 ($ Million) Figure 10: Global Big Data NoSQL Submarket Revenue: 2010 - 2020 ($ Million) Figure 11: Global Big Data Analytic Platforms & Applications Submarket Reve= nue: 2010 - 2020 ($ Million) Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2010 - 2020 (= $ Million) Figure 13: Global Big Data Professional Services Submarket Revenue: 2010 - = 2020 ($ Million) Figure 14: Global Big Data Revenue by Vertical Market: 2010 - 2020 ($ Milli= on) Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transport= ation Sector: 2010 - 2020 ($ Million) Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2010= - 2020 ($ Million) Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 20= 10 - 2020 ($ Million) Figure 18: Global Big Data Revenue in the Education Sector: 2010 - 2020 ($ = Million) Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Secto= r: 2010 - 2020 ($ Million) Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildi= ngs Sector: 2010 - 2020 ($ Million) Figure 21: Global Big Data Revenue in the Insurance Sector: 2010 - 2020 ($ = Million) Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources= Sector: 2010 - 2020 ($ Million) Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 201= 0 - 2020 ($ Million) Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security= Sector: 2010 - 2020 ($ Million) Figure 25: Global Big Data Revenue in the Public Services Sector: 2010 - 20= 20 ($ Million) Figure 26: Global Big Data Revenue in the Retail & Hospitality Sector: 2010= - 2020 ($ Million) Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2010 -= 2020 ($ Million) Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2010 -= 2020 ($ Million) Figure 29: Global Big Data Revenue in the Wholesale Trade Sector: 2010 - 20= 20 ($ Million) Figure 30: Global Big Data Revenue in Other Vertical Sectors: 2010 - 2020 (= $ Million) Figure 31: Big Data Revenue by Region: 2010 - 2020 ($ Million) Figure 32: Asia Pacific Big Data Revenue: 2010 - 2020 ($ Million) Figure 33: Asia Pacific Big Data Revenue by Country: 2010 - 2020 ($ Million) Figure 34: Australia Big Data Revenue: 2010 - 2020 ($ Million) Figure 35: China Big Data Revenue: 2010 - 2020 ($ Million) Figure 36: India Big Data Revenue: 2010 - 2020 ($ Million) Figure 37: Japan Big Data Revenue: 2010 - 2020 ($ Million) Figure 38: South Korea Big Data Revenue: 2010 - 2020 ($ Million) Figure 39: Pakistan Big Data Revenue: 2010 - 2020 ($ Million) Figure 40: Thailand Big Data Revenue: 2010 - 2020 ($ Million) Figure 41: Indonesia Big Data Revenue: 2010 - 2020 ($ Million) Figure 42: Malaysia Big Data Revenue: 2010 - 2020 ($ Million) Figure 43: Taiwan Big Data Revenue: 2010 - 2020 ($ Million) Figure 44: Philippines Big Data Revenue: 2010 - 2020 ($ Million) Figure 45: Singapore Big Data Revenue: 2010 - 2020 ($ Million) Figure 46: Big Data Revenue in the Rest of Asia Pacific: 2010 - 2020 ($ Mil= lion) Figure 47: Eastern Europe Big Data Revenue: 2010 - 2020 ($ Million) Figure 48: Eastern Europe Big Data Revenue by Country: 2010 - 2020 ($ Milli= on) Figure 49: Czech Republic Big Data Revenue: 2010 - 2020 ($ Million) Figure 50: Poland Big Data Revenue: 2010 - 2020 ($ Million) Figure 51: Russia Big Data Revenue: 2010 - 2020 ($ Million) Figure 52: Big Data Revenue in the Rest of Eastern Europe: 2010 - 2020 ($ M= illion) Figure 53: Latin & Central America Big Data Revenue: 2010 - 2020 ($ Million) Figure 54: Latin & Central America Big Data Revenue by Country: 2010 - 2020= ($ Million) Figure 55: Argentina Big Data Revenue: 2010 - 2020 ($ Million) Figure 56: Brazil Big Data Revenue: 2010 - 2020 ($ Million) Figure 57: Mexico Big Data Revenue: 2010 - 2020 ($ Million) Figure 58: Big Data Revenue in the Rest of Latin & Central America: 2010 - = 2020 ($ Million) Figure 59: Middle East & Africa Big Data Revenue: 2010 - 2020 ($ Million) Figure 60: Middle East & Africa Big Data Revenue by Country: 2010 - 2020 ($= Million) Figure 61: South Africa Big Data Revenue: 2010 - 2020 ($ Million) Figure 62: UAE Big Data Revenue: 2010 - 2020 ($ Million) Figure 63: Qatar Big Data Revenue: 2010 - 2020 ($ Million) Figure 64: Saudi Arabia Big Data Revenue: 2010 - 2020 ($ Million) Figure 65: Israel Big Data Revenue: 2010 - 2020 ($ Million) Figure 66: Big Data Revenue in the Rest of the Middle East & Africa: 2010 -= 2020 ($ Million) Figure 67: North America Big Data Revenue: 2010 - 2020 ($ Million) Figure 68: North America Big Data Revenue by Country: 2010 - 2020 ($ Millio= n) Figure 69: USA Big Data Revenue: 2010 - 2020 ($ Million) Figure 70: Canada Big Data Revenue: 2010 - 2020 ($ Million) Figure 71: Western Europe Big Data Revenue: 2010 - 2020 ($ Million) Figure 72: Western Europe Big Data Revenue by Country: 2010 - 2020 ($ Milli= on) Figure 73: Denmark Big Data Revenue: 2010 - 2020 ($ Million) Figure 74: Finland Big Data Revenue: 2010 - 2020 ($ Million) Figure 75: France Big Data Revenue: 2010 - 2020 ($ Million) Figure 76: Germany Big Data Revenue: 2010 - 2020 ($ Million) Figure 77: Italy Big Data Revenue: 2010 - 2020 ($ Million) Figure 78: Spain Big Data Revenue: 2010 - 2020 ($ Million) Figure 79: Sweden Big Data Revenue: 2010 - 2020 ($ Million) Figure 80: Norway Big Data Revenue: 2010 - 2020 ($ Million) Figure 81: UK Big Data Revenue: 2010 - 2020 ($ Million) Figure 82: Big Data Revenue in the Rest of Western Europe: 2010 - 2020 ($ M= illion) Figure 83: Global Big Data Revenue by Hardware, Software & Professional Ser= vices ($ Million): 2010 - 2020 Figure 84: Big Data Vendor Market Share (%) Figure 85: Global IT Expenditure Driven by Big Data Investments: 2010 - 202= 0 ($ Million) Figure 86: Global M2M Connections by Access Technology (Millions): 2011 - 2= 020 List of Companies Mentioned: 1010data Accel Partners Accenture Actian Corporation Actuate Corporation adMarketplace Adobe ADP AeroSpike AlchemyDB Aldeasa Alpine Data Labs Alteryx Amazon.com AMD AnalyticsIQ Antic Entertainment AOL Apple AppNexus Ascendas AT&T Attivio AutoZone Avvasi AWS (Amazon Web Services) Axiata Group Bank of America Basho Beeline Kazakhstan Betfair BlueKai Bluelock BMC Software BMW Boeing Booz Allen Hamilton Box, Inc. Buffalo Studios BurstaBit CaixaTarragona Capgemini Cellwize CenturyLink Chang China Telecom CIA (Central Intelligence Agency) Cisco Systems Citywire Cloudera Coca-Cola Comptel Concur Contexti Coriant Couchbase CSA (Cloud Security Alliance) CSC (Computer Science Corporation) CSCC (Cloud Standards Customer Council) Datameer DataStax DDN (DataDirect Network) Dell Deloitte Delta Department of Commerce Deutsche Bank Deutsche Telekom Digital Reasoning Dollar General Dotomi eBay El Corte Ingl=E9s Electronic Arts EMC Corporation Equifax Ericsson Ernst & Young E-Touch European Space Agency eXelate Experian Facebook FedEx Ferguson Ford Fractal Analytics Fujitsu Fusion-io Gamegos Ganz GE (General Electric) Goldman Sachs GoodData Corporation Google Greylock Partners GTRI (Georgia Tech Research Institute) Guavus Hadapt HDS (Hitachi Data Systems) Hortonworks HP Hyve Solutions IBM IEC (International Electrotechnical Commission) Ignition Partners InfiniDB Infobright Informatica Corporation Information Builders In-Q-Tel Intel Internap Network Services Corporation Intucell Inversis Banco ISO (International Organization for Standardization) ITT Corporation ITU (International Telecommunications Union) J.P. Morgan Jaspersoft Johnson & Johnson JP Morgan Juguettos Juniper Networks Kabam Karmasphere KDDI Kixeye Kobo Kognitio KPMG KT (Korea Telecom) Lavastorm Analytics LG CNS LinkedIn LucidWorks Mahindra Satyam MapR MarkLogic Marriott International Mayfield fund McDonnell Ventures McGraw Hill Education MediaMind Meritech Capital Partners Microsoft MicroStrategy mig33 MongoDB Motorola Movistar Mu Sigma Myrrix Nami Media Navteq Neo Technology NetApp NetFlix Nexon NIST (National Institute of Standards and Technology) North Bridge NTT Data NTT DoCoMo NYSE (New York Stock Exchange) OASIS ODaF (Open Data Foundation) Open Data Center Alliance Opera Solutions Oracle Orange Orbitz Palantir Technologies Panorama Software ParAccel ParStream Pentaho Pervasive Software Pivotal Software Platfora Playtika Pokemon Proctor and Gamble Pronovias PwC QlikTech Quantum Corporation Quiterian Rackspace RainStor Relational Technology Renault ReNet Tecnologia Rentrak Revolution Analytics RiteAid Robi Axiata Royal Dutch Shell Sabre Sailthru Sain Engineering Salesforce.com Samsung SAP SAS Institute Savvis Scoreloop Seagate Technology SGI Shuffle Master Simba Technologies SiSense Skyscanner SmugMug Snapdeal Software AG Sojo Studios SolveDirect Sony Southern States Cooperative Splunk Spotme Sqrrl Starbucks Supermicro Tableau Software Talend Tango TapJoy TCS (Tata Consultancy Services) Telef=F3nica Tencent Teradata Terracotta Terremark The Hut Group The Knot The Ladders The Trade Desk Think Big Analytics Thomson Reuters TIBCO Software Tidemark TubeMogul Tunewiki U.S. Air Force U.S. Army U.S. Navy Ubiquisys UBS Umami TV UN (United Nations) Unilever US Xpress Venture Partners Verizon Versant Vertica VIMPELCOM VMware (EMC Subsidiary) VNG Vodafone Volkswagen Walt Disney Company WIND Mobile WiPro Xclaim Xyratex Yael Software Zettics Zynga Report Pricing: Single User License: USD 2,500 Company Wide License (Multi Users): USD 3,500 Ordering Process: Please contact Andy Silva on andy.silva@snsreports.com And provide the following information: Report Title - 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 I look forward to hearing from you. Kind Regards Andy Silva Marketing Executive Signals and Systems Telecom Reef Tower Jumeirah Lake Towers Sheikh Zayed Road Dubai, UAE =20 To unsubscribe please click on the link below or send an email with unsubsc= ribe in the subject line to: info@snsreports.com Remove me from your mailing list
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