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The Big Data Market: 2016 2030 Opportunities, Challenges, Strategies, Industry Verticals and Forecasts

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“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.
 
 Amid the proliferation of real time data from sources such as mobile devices, web, social media, sensors, log files and transactional applications, Big Data has found a host of vertical market applications, ranging from fraud detection to scientific R&D.
 
 Despite challenges relating to privacy concerns and organizational resistance, Big Data investments continue to gain momentum throughout the globe. SNS Research estimates that Big Data investments will account for over $46 Billion in 2016 alone. These investments are further expected to grow at a CAGR of 12% over the next four years.
 
 The “Big Data Market: 2016 – 2030 – Opportunities, Challenges, Strategies, Industry Verticals & Forecasts” report presents an in-depth assessment of the Big Data ecosystem including key market drivers, challenges, investment potential, vertical market opportunities and use cases, future roadmap, value chain, case studies on Big Data analytics, vendor market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2016 through to 2030. The forecasts are further segmented for 8 horizontal submarkets, 14 vertical markets, 6 regions and 35 countries.
 
 The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.
 
 Topics Covered
 The report covers the following topics:
 - Big Data ecosystem
 - Market drivers and barriers
 - Big Data technology, standardization and regulatory initiatives
 - Big Data industry roadmap and value chain
 - Analysis and use cases for 14 vertical markets
 - Big Data analytics technology and case studies
 - Big Data vendor market share
 - Company profiles and strategies of 150 Big Data ecosystem players
 - Strategic recommendations for Big Data hardware, software and professional services vendors and enterprises
 - Market analysis and forecasts from 2016 till 2030
 
 Historical Revenue & Forecast Segmentation
 Market forecasts and historical revenue figures are provided for each of the following submarkets and their subcategories:
 
 Hardware, Software & Professional Services
 - Hardware
 - Software
 - Professional Services
 
 Horizontal Submarkets
 - Storage & Compute Infrastructure
 - Networking Infrastructure
 - Hadoop & Infrastructure Software
 - SQL
 - NoSQL
 - Analytic Platforms & Applications
 - Cloud Platforms
 - Professional Services
 
 Vertical Submarkets
 - Automotive, Aerospace & Transportation
 - Banking & Securities
 - Defense & Intelligence
 - Education
 - Healthcare & Pharmaceutical
 - Smart Cities & Intelligent Buildings
 - Insurance
 - Manufacturing & Natural Resources
 - Web, Media & Entertainment
 - Public Safety & Homeland Security
 - Public Services
 - Retail, Wholesale & Hospitality
 - Telecommunications
 - Utilities & Energy
 - Others
 
 Regional Markets
 - Asia Pacific
 - Eastern Europe
 - Latin & Central America
 - Middle East & Africa
 - North America
 - Western Europe
 
 Country Markets
 
 - Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK, USA
 
 Key Questions Answered
 
 The report provides answers to the following key questions:
 - How big is the Big Data ecosystem?
 - How is the ecosystem evolving by segment and region?
 - What will the market size be in 2020 and at what rate will it grow?
 - What trends, challenges and barriers are influencing its growth?
 - Who are the key Big Data software, hardware and services vendors and what are their strategies?
 - How much are vertical enterprises investing in Big Data?
 - What opportunities exist for Big Data analytics?
 - Which countries and verticals will see the highest percentage of Big Data investments?
 
 Key Findings
 
 The report has the following key findings:
 - In 2016, Big Data vendors will pocket over $46 Billion from hardware, software and professional services revenues.
 - Big Data investments are further expected to grow at a CAGR of 12% over the next four years, eventually accounting for over $72 Billion by the end of 2020.
 - The market is ripe for acquisitions of pure-play Big Data startups, as competition heats up between IT incumbents.
 - Nearly every large scale IT vendor maintains a Big Data portfolio.
 - At present, the market is largely dominated by hardware sales and professional services in terms of revenue.
 - Going forward, software vendors, particularly those in the Big Data analytics segment, are expected to significantly increase their stake in the Big Data market.
 - By the end of 2020, SNS Research expects Big Data software revenue to exceed hardware investments by over $7 Billion.

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?
 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
 
 3 Chapter 3: Big Data Analytics
 3.1 What are Big Data Analytics?
 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
 
 4 Chapter 4: Big Data in Automotive, Aerospace & Transportation
 4.1 Overview & Investment Potential
 4.2 Key Applications
 4.2.1 Warranty Analytics for Automotive OEMs
 4.2.2 Predictive Aircraft Maintenance & Fuel Optimization
 4.2.3 Air Traffic Control
 4.2.4 Transport Fleet Optimization
 4.3 Case Studies
 4.3.1 Boeing: Making Flying More Efficient with Big Data
 4.3.2 BMW: Eliminating Defects in New Vehicle Models with Big Data
 4.3.3 Toyota Motor Corporation: Powering Smart Cars with Big Data
 4.3.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data
 
 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
 
 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 Threats
 6.3.4 Chinese Ministry of State Security: Predictive Policing with Big Data
 6.3.5 French DGSE (General Directorate for External Security): Enhancing Intelligence with Big Data
 
 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
 
 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 Novartis: Digitizing Healthcare with Big Data
 8.3.2 GSK (GlaxoSmithKline): Accelerating Drug Discovering with Big Data
 8.3.3 Pfizer: Developing Effective and Targeted Therapies with Big Data
 8.3.4 Roche: Personalizing Healthcare with Big Data
 8.3.5 Sanofi: Proactive Diabetes Care with Big Data
 
 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’s Most Intelligent Building with Big Data
 
 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
 
 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
 
 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 NFL (National Football League): Improving Stadium Experience with Big Data
 12.3.2 Walt Disney Company: Enhancing Theme Park Experience with Big Data
 12.3.3 Baidu: Reshaping Search Capabilities with Big Data
 12.3.4 Constant Contact: Effective Marketing with Big Data
 
 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 U.S. DHS (Department of Homeland Security): Identifying Threats to Physical 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
 
 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
 14.3 Case Studies
 14.3.1 New York State Department of Taxation and Finance: Increasing Tax Revenue with Big Data
 14.3.2 Alameda County Social Services Agency: Benefit Fraud Reduction with Big Data
 14.3.3 City of Chicago: Improving Government Productivity with Big Data
 14.3.4 FDNY (Fire Department of the City of New York): Fighting Fires with Big Data
 14.3.5 Ambulance Victoria: Improving Patient Survival Rates with Big Data
 
 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
 
 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 WIND Mobile: Optimizing Video Quality with Big Data
 16.3.6 Coriant: SaaS Based Analytics with Big Data
 
 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
 
 18 Chapter 18: Big Data Industry Roadmap & Value Chain
 18.1 Big Data Industry Roadmap
 18.1.1 2010 – 2013: Initial Hype and the Rise of Analytics
 18.1.2 2014 – 2017: Emergence of SaaS Based Big Data Solutions
 18.1.3 2018 – 2020: Growing Adoption of Scalable Machine Learning
 18.1.4 2021 & Beyond: Widespread Investments on Cognitive & Personalized Analytics
 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
 
 19 Chapter 19: Standardization & Regulatory Initiatives
 19.1 CSCC (Cloud Standards Customer Council) – Big Data Working Group
 19.2 NIST (National Institute of Standards and Technology) – Big Data Working Group
 19.3 OASIS –Technical Committees
 19.4 ODaF (Open Data Foundation)
 19.5 Open Data Center Alliance
 19.6 CSA (Cloud Security Alliance) – Big Data Working Group
 19.7 ITU (International Telecommunications Union)
 19.8 ISO (International Organization for Standardization) and Others
 
 20 Chapter 20: Market Analysis & Forecasts
 20.1 Global Outlook of the Big Data Market
 20.2 Submarket Segmentation
 20.2.1 Storage and Compute Infrastructure
 20.2.2 Networking Infrastructure
 20.2.3 Hadoop & Infrastructure Software
 20.2.4 SQL
 20.2.5 NoSQL
 20.2.6 Analytic Platforms & Applications
 20.2.7 Cloud Platforms
 20.2.8 Professional Services
 20.3 Vertical Market Segmentation
 20.3.1 Automotive, Aerospace & Transportation
 20.3.2 Banking & Securities
 20.3.3 Defense & Intelligence
 20.3.4 Education
 20.3.5 Healthcare & Pharmaceutical
 20.3.6 Smart Cities & Intelligent Buildings
 20.3.7 Insurance
 20.3.8 Manufacturing & Natural Resources
 20.3.9 Media & Entertainment
 20.3.10 Public Safety & Homeland Security
 20.3.11 Public Services
 20.3.12 Retail, Wholesale & Hospitality
 20.3.13 Telecommunications
 20.3.14 Utilities & Energy
 20.3.15 Other Sectors
 20.4 Regional Outlook
 20.5 Asia Pacific
 20.5.1 Country Level Segmentation
 20.5.2 Australia
 20.5.3 China
 20.5.4 India
 20.5.5 Indonesia
 20.5.6 Japan
 20.5.7 Malaysia
 20.5.8 Pakistan
 20.5.9 Philippines
 20.5.10 Singapore
 20.5.11 South Korea
 20.5.12 Taiwan
 20.5.13 Thailand
 20.5.14 Rest of Asia Pacific
 20.6 Eastern Europe
 20.6.1 Country Level Segmentation
 20.6.2 Czech Republic
 20.6.3 Poland
 20.6.4 Russia
 20.6.5 Rest of Eastern Europe
 20.7 Latin & Central America
 20.7.1 Country Level Segmentation
 20.7.2 Argentina
 20.7.3 Brazil
 20.7.4 Mexico
 20.7.5 Rest of Latin & Central America
 20.8 Middle East & Africa
 20.8.1 Country Level Segmentation
 20.8.2 Israel
 20.8.3 Qatar
 20.8.4 Saudi Arabia
 20.8.5 South Africa
 20.8.6 UAE
 20.8.7 Rest of the Middle East & Africa
 20.9 North America
 20.9.1 Country Level Segmentation
 20.9.2 Canada
 20.9.3 USA
 20.10 Western Europe
 20.10.1 Country Level Segmentation
 20.10.2 Denmark
 20.10.3 Finland
 20.10.4 France
 20.10.5 Germany
 20.10.6 Italy
 20.10.7 Netherlands
 20.10.8 Norway
 20.10.9 Spain
 20.10.10 Sweden
 20.10.11 UK
 20.10.12 Rest of Western Europe
 
 21 Chapter 21: Vendor Landscape
 21.1 1010data
 21.2 Accenture
 21.3 Actian Corporation
 21.4 Actuate Corporation
 21.5 Adaptive Insights
 21.6 Advizor Solutions
 21.7 AeroSpike
 21.8 AFS Technologies
 21.9 Alpine Data Labs
 21.10 Alteryx
 21.11 Altiscale
 21.12 Antivia
 21.13 Arcplan
 21.14 Attivio
 21.15 Automated Insights
 21.16 AWS (Amazon Web Services)
 21.17 Ayasdi
 21.18 Basho
 21.19 BeyondCore
 21.20 Birst
 21.21 Bitam
 21.22 Board International
 21.23 Booz Allen Hamilton
 21.24 Capgemini
 21.25 Cellwize
 21.26 Centrifuge Systems
 21.27 CenturyLink
 21.28 Chartio
 21.29 Cisco Systems
 21.30 ClearStory Data
 21.31 Cloudera
 21.32 Comptel
 21.33 Concurrent
 21.34 Contexti
 21.35 Couchbase
 21.36 CSC (Computer Science Corporation)
 21.37 DataHero
 21.38 Datameer
 21.39 DataRPM
 21.40 DataStax
 21.41 Datawatch Corporation
 21.42 DDN (DataDirect Network)
 21.43 Decisyon
 21.44 Dell
 21.45 Deloitte
 21.46 Denodo Technologies
 21.47 Digital Reasoning
 21.48 Dimensional Insight
 21.49 Domo
 21.50 Dundas Data Visualization
 21.51 Eligotech
 21.52 EMC Corporation
 21.53 Engineering Group (Engineering Ingegneria Informatica)
 21.54 eQ Technologic
 21.55 Facebook
 21.56 FICO
 21.57 Fractal Analytics
 21.58 Fujitsu
 21.59 Fusion-io
 21.60 GE (General Electric)
 21.61 GoodData Corporation
 21.62 Google
 21.63 Guavus
 21.64 HDS (Hitachi Data Systems)
 21.65 Hortonworks
 21.66 HPE (Hewlett Packard Enterprise)
 21.67 IBM
 21.68 iDashboards
 21.69 Incorta
 21.70 InetSoft Technology Corporation
 21.71 InfiniDB
 21.72 Infor
 21.73 Informatica Corporation
 21.74 Information Builders
 21.75 Intel
 21.76 Jedox
 21.77 Jinfonet Software
 21.78 Juniper Networks
 21.79 Knime
 21.80 Kofax
 21.81 Kognitio
 21.82 L-3 Communications
 21.83 Lavastorm Analytics
 21.84 Logi Analytics
 21.85 Looker Data Sciences
 21.86 LucidWorks
 21.87 Maana
 21.88 Manthan Software Services
 21.89 MapR
 21.90 MarkLogic
 21.91 MemSQL
 21.92 Microsoft
 21.93 MicroStrategy
 21.94 MongoDB (formerly 10gen)
 21.95 Mu Sigma
 21.96 NTT Data
 21.97 Neo Technology
 21.98 NetApp
 21.99 Nutonian
 21.100 OpenText Corporation
 21.101 Opera Solutions
 21.102 Oracle
 21.103 Palantir Technologies
 21.104 Panorama Software
 21.105 ParStream
 21.106 Pentaho
 21.107 Phocas
 21.108 Pivotal Software
 21.109 Platfora
 21.110 Prognoz
 21.111 PwC
 21.112 Pyramid Analytics
 21.113 Qlik
 21.114 Quantum Corporation
 21.115 Qubole
 21.116 Rackspace
 21.117 RapidMiner
 21.118 Recorded Future
 21.119 RJMetrics
 21.120 Salesforce.com
 21.121 Sailthru
 21.122 Salient Management Company
 21.123 SAP
 21.124 SAS Institute
 21.125 SGI
 21.126 SiSense
 21.127 Software AG
 21.128 Splice Machine
 21.129 Splunk
 21.130 Sqrrl
 21.131 Strategy Companion
 21.132 Supermicro
 21.133 Syncsort
 21.134 SynerScope
 21.135 Tableau Software
 21.136 Talend
 21.137 Targit
 21.138 TCS (Tata Consultancy Services)
 21.139 Teradata
 21.140 Think Big Analytics
 21.141 ThoughtSpot
 21.142 TIBCO Software
 21.143 Tidemark
 21.144 VMware (EMC Subsidiary)
 21.145 WiPro
 21.146 Yellowfin International
 21.147 Zendesk
 21.148 Zettics
 21.149 Zoomdata
 21.150 Zucchetti
 
 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?
 22.4 Will Regulation have a Negative Impact on Big Data Investments?
 22.5 Battling Organization & Data Silos
 22.6 Software vs. Hardware Investments
 22.7 Vendor Share: Who Leads the Market?
 22.8 Big Data Driving Wider IT Industry Investments
 22.9 Assessing the Impact of IoT & M2M
 22.10 Recommendations
 22.10.1 Big Data Hardware, Software & Professional Services Providers
 22.10.2 Enterprises


List Of Figures

 Figure 1: Reactive vs. Proactive Analytics
 Figure 2: Big Data Industry Roadmap
 Figure 3: The Big Data Value Chain
 Figure 4: Global Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 5: Global Big Data Revenue by Submarket: 2016 - 2030 ($ Million)
 Figure 6: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2016 - 2030 ($ Million)
 Figure 7: Global Big Data Networking Infrastructure Submarket Revenue: 2016 - 2030 ($ Million)
 Figure 8: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2016 - 2030 ($ Million)
 Figure 9: Global Big Data SQL Submarket Revenue: 2016 - 2030 ($ Million)
 Figure 10: Global Big Data NoSQL Submarket Revenue: 2016 - 2030 ($ Million)
 Figure 11: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2016 - 2030 ($ Million)
 Figure 12: Global Big Data Cloud Platforms Submarket Revenue: 2016 - 2030 ($ Million)
 Figure 13: Global Big Data Professional Services Submarket Revenue: 2016 - 2030 ($ Million)
 Figure 14: Global Big Data Revenue by Vertical Market: 2016 - 2030 ($ Million)
 Figure 15: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2016 - 2030 ($ Million)
 Figure 16: Global Big Data Revenue in the Banking & Securities Sector: 2016 - 2030 ($ Million)
 Figure 17: Global Big Data Revenue in the Defense & Intelligence Sector: 2016 - 2030 ($ Million)
 Figure 18: Global Big Data Revenue in the Education Sector: 2016 - 2030 ($ Million)
 Figure 19: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2016 - 2030 ($ Million)
 Figure 20: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2016 - 2030 ($ Million)
 Figure 21: Global Big Data Revenue in the Insurance Sector: 2016 - 2030 ($ Million)
 Figure 22: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2016 - 2030 ($ Million)
 Figure 23: Global Big Data Revenue in the Media & Entertainment Sector: 2016 - 2030 ($ Million)
 Figure 24: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2016 - 2030 ($ Million)
 Figure 25: Global Big Data Revenue in the Public Services Sector: 2016 - 2030 ($ Million)
 Figure 26: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2016 - 2030 ($ Million)
 Figure 27: Global Big Data Revenue in the Telecommunications Sector: 2016 - 2030 ($ Million)
 Figure 28: Global Big Data Revenue in the Utilities & Energy Sector: 2016 - 2030 ($ Million)
 Figure 29: Global Big Data Revenue in Other Vertical Sectors: 2016 - 2030 ($ Million)
 Figure 30: Big Data Revenue by Region: 2016 - 2030 ($ Million)
 Figure 31: Asia Pacific Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 32: Asia Pacific Big Data Revenue by Country: 2016 - 2030 ($ Million)
 Figure 33: Australia Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 34: China Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 35: India Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 36: Indonesia Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 37: Japan Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 38: Malaysia Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 39: Pakistan Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 40: Philippines Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 41: Singapore Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 42: South Korea Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 43: Taiwan Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 44: Thailand Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 45: Big Data Revenue in the Rest of Asia Pacific: 2016 - 2030 ($ Million)
 Figure 46: Eastern Europe Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 47: Eastern Europe Big Data Revenue by Country: 2016 - 2030 ($ Million)
 Figure 48: Czech Republic Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 49: Poland Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 50: Russia Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 51: Big Data Revenue in the Rest of Eastern Europe: 2016 - 2030 ($ Million)
 Figure 52: Latin & Central America Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 53: Latin & Central America Big Data Revenue by Country: 2016 - 2030 ($ Million)
 Figure 54: Argentina Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 55: Brazil Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 56: Mexico Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 57: Big Data Revenue in the Rest of Latin & Central America: 2016 - 2030 ($ Million)
 Figure 58: Middle East & Africa Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 59: Middle East & Africa Big Data Revenue by Country: 2016 - 2030 ($ Million)
 Figure 60: Israel Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 61: Qatar Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 62: Saudi Arabia Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 63: South Africa Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 64: UAE Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 65: Big Data Revenue in the Rest of the Middle East & Africa: 2016 - 2030 ($ Million)
 Figure 66: North America Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 67: North America Big Data Revenue by Country: 2016 - 2030 ($ Million)
 Figure 68: Canada Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 69: USA Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 70: Western Europe Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 71: Western Europe Big Data Revenue by Country: 2016 - 2030 ($ Million)
 Figure 72: Denmark Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 73: Finland Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 74: France Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 75: Germany Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 76: Italy Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 77: Netherlands Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 78: Norway Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 79: Spain Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 80: Sweden Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 81: UK Big Data Revenue: 2016 - 2030 ($ Million)
 Figure 82: Big Data Revenue in the Rest of Western Europe: 2016 - 2030 ($ Million)
 Figure 83: Global Big Data Revenue by Hardware, Software & Professional Services: 2016 – 2030 ($ Million)
 Figure 84: Big Data Vendor Market Share (%)
 Figure 85: Global IT Expenditure Driven by Big Data Investments: 2016 - 2030 ($ Million)
 Figure 86: Global M2M Connections by Access Technology: 2016 – 2030 (Millions)


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