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The Big Data Market: 2017 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 $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 four 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 Dells $60 Billion merger with data storage specialist EMC.
  

 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
 
 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 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 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 Ministry of State Security, China: 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 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
 
 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 Netflix: Improving Viewership with Big Data
 12.3.2 NFL (National Football League): Improving Stadium Experience with Big 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
 
 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 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
 14.2.4 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 Revenue 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
 
 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 Freedom 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 2017 – 2020: Investments in Predictive Analytics & SaaS-Based Big Data Offerings
 18.1.2 2020 – 2025: Growing Focus on Cognitive & Personalized Analytics
 18.1.3 2025 – 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
 
 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) –Big Data Initiative
 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 – Cloud Computing Based Requirements and Capabilities
 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 Standards)
 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 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
 
 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
 
 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 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?
 22.9 Moving Towards Consolidation: Review of M&A Activity in the Vendor Arena
 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


List Of Figures

 

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 Revenue: 2017 - 2030 ($ Million)
Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2017 - 2030 ($ Million)
Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 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 Revenue: 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 ($ Million)
Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transportation 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: 2017 - 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 Sector: 2017 - 2030 ($ Million)
Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildings 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: 2017 - 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 - 2030 ($ Million)
Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 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 ($ Million)
Figure 48: Eastern Europe Big Data Revenue: 2017 - 2030 ($ Million)
Figure 49: Eastern Europe Big Data Revenue by Country: 2017 - 2030 ($ Million)
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 ($ Million)
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 ($ Million)
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 ($ Million)
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 ($ Million)
Figure 85: Global Big Data Workload Distribution by Environment: 2017 - 2030 (%)
Figure 86: Global Big Data Revenue by Hardware, Software & Professional Services: 2017 - 2030 ($ Million)
Figure 87: Big Data Vendor Market Share (%)
Figure 88: Global IT Expenditure Driven by Big Data Investments: 2017 - 2030 ($ Million)
Figure 89: Global M2M Connections by Access Technology: 2017 - 2030 (Millions)
 

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
  


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