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Market Research Report

The Big Data Market: 2018 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts

Published by SNS Telecom & IT Product code 650528
Published Content info 549 Pages
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The Big Data Market: 2018 - 2030 - Opportunities, Challenges, Strategies, Industry Verticals & Forecasts
Published: June 12, 2018 Content info: 549 Pages
Description

"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 Telecom & IT estimates that Big Data investments will account for over $65 Billion in 2018 alone. These investments are further expected to grow at a CAGR of approximately 14% over the next three years.

The "Big Data Market: 2018 - 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 profiles, market share and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services from 2018 till 2030. The forecasts are 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
  • Enabling technologies, standardization and regulatory initiatives
  • Big Data analytics and implementation models
  • Key trends - including AI (Artificial Intelligence), machine learning, edge analytics, cloud-based Big Data platforms, and the impact of the IoT (Internet of Things)
  • Analysis of key applications and investment potential for 14 vertical markets
  • Over 60 case studies on the use of Big Data and analytics
  • Big Data vendor market share
  • Future roadmap and value chain
  • Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
  • Strategic recommendations for Big Data hardware, software and professional services vendors, and enterprises
  • Market analysis and forecasts from 2018 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 2021, 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 2018, Big Data vendors will pocket over $65 Billion from hardware, software and professional services revenues. These investments are further expected to grow at a CAGR of approximately 14% over the next three years, eventually accounting for more than $96 Billion by the end of 2021.
  • With ongoing advances in AI (Artificial Intelligence) technologies, Big Data analytics initiatives are beginning to leverage sophisticated deep learning systems with an autonomous sense of judgment - to enable a range of applications from chatbots and virtual assistants to self-driving vehicles and precision medicine.
  • In order to analyze data closer to where it is collected, Big Data and advanced analytics technologies are increasingly being integrated into edge environments, including network nodes, numerous industrial machines and IoT (Internet of Things) devices.
  • The vendor arena is continuing to consolidate with several prominent M&A deals such as Oracle's recent acquisition of enterprise data science platform provider DataScience.com - in a bid to beef up its capabilities in machine learning and Big Data for predictive analytics, and Google's acquisition of Big Data application platform provider Cask Data.
Table of Contents

Table of Contents

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 Graph Analytics
    • 3.5.10 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 & Semi-Autonomous Driving
    • 4.2.2 Streamlining Vehicle Recalls & Warranty Management
    • 4.2.3 Fleet Management
    • 4.2.4 Intelligent Transportation
    • 4.2.5 UBI (Usage Based Insurance)
    • 4.2.6 Predictive Aircraft Maintenance & Fuel Optimization
    • 4.2.7 Air Traffic Control
  • 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 Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data
    • 4.3.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data
    • 4.3.5 Groupe Renault: Boosting Driver Safety with Big Data
    • 4.3.6 Honda Motor Company: Improving F1 Performance & Fuel Efficiency 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 Products
    • 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: Improving Academic Performance 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 Drug Discovery, Design & Development
    • 8.2.2 Clinical Development & Trials
    • 8.2.3 Population Health Management
    • 8.2.4 Personalized Healthcare & Targeted Treatments
    • 8.2.5 Proactive & Remote Patient Monitoring
    • 8.2.6 Preventive Care & Health Interventions
  • 8.3 Case Studies
    • 8.3.1 AstraZeneca: Analytics-Driven Drug Development with Big Data
    • 8.3.2 Bangkok Hospital Group: Transforming the Patient Experience with Big Data
    • 8.3.3 Novartis: Digitizing Healthcare with Big Data
    • 8.3.4 Pfizer: Developing Effective and Targeted Therapies with Big Data
    • 8.3.5 Sanofi: Proactive Diabetes Care with Big Data
    • 8.3.6 UnitedHealth Group: Enhancing Patient Care & Value 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 Twitter: Cracking Down on Abusive Content with Big Data
    • 12.3.2 Netflix: Improving Viewership with Big Data
    • 12.3.3 NFL (National Football League): Improving Stadium 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 (Department of Homeland Security): Identifying Threats 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 The Walt Disney Company: Theme Park Management with Big Data
    • 15.3.4 Marriott International: Elevating Guest Services with Big Data
    • 15.3.5 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: Future Roadmap & Value Chain

  • 18.1 Future Roadmap
    • 18.1.1 Pre-2020: Towards Cloud-Based Big Data Offerings for Advanced Analytics
    • 18.1.2 2020 - 2025: Growing Focus on AI (Artificial Intelligence), Deep Learning & Edge 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)
    • 19.5.1 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 Telecommunication 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 Sizing & 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/HCL Technologies
  • 21.5 Adaptive Insights
  • 21.6 Adobe Systems
  • 21.7 Advizor Solutions
  • 21.8 AeroSpike
  • 21.9 AFS Technologies
  • 21.10 Alation
  • 21.11 Algorithmia
  • 21.12 Alluxio
  • 21.13 ALTEN
  • 21.14 Alteryx
  • 21.15 AMD (Advanced Micro Devices)
  • 21.16 Anaconda
  • 21.17 Apixio
  • 21.18 Arcadia Data
  • 21.19 ARM
  • 21.20 AtScale
  • 21.21 Attivio
  • 21.22 Attunity
  • 21.23 Automated Insights
  • 21.24 AVORA
  • 21.25 AWS (Amazon Web Services)
  • 21.26 Axiomatics
  • 21.27 Ayasdi
  • 21.28 BackOffice Associates
  • 21.29 Basho Technologies
  • 21.30 BCG (Boston Consulting Group)
  • 21.31 Bedrock Data
  • 21.32 BetterWorks
  • 21.33 Big Panda
  • 21.34 BigML
  • 21.35 Bitam
  • 21.36 Blue Medora
  • 21.37 BlueData Software
  • 21.38 BlueTalon
  • 21.39 BMC Software
  • 21.40 BOARD International
  • 21.41 Booz Allen Hamilton
  • 21.42 Boxever
  • 21.43 CACI International
  • 21.44 Cambridge Semantics
  • 21.45 Capgemini
  • 21.46 Cazena
  • 21.47 Centrifuge Systems
  • 21.48 CenturyLink
  • 21.49 Chartio
  • 21.50 Cisco Systems
  • 21.51 Civis Analytics
  • 21.52 ClearStory Data
  • 21.53 Cloudability
  • 21.54 Cloudera
  • 21.55 Cloudian
  • 21.56 Clustrix
  • 21.57 CognitiveScale
  • 21.58 Collibra
  • 21.59 Concurrent Technology/Vecima Networks
  • 21.60 Confluent
  • 21.61 Contexti
  • 21.62 Couchbase
  • 21.63 Crate.io
  • 21.64 Cray
  • 21.65 Databricks
  • 21.66 Dataiku
  • 21.67 Datalytyx
  • 21.68 Datameer
  • 21.69 DataRobot
  • 21.70 DataStax
  • 21.71 Datawatch Corporation
  • 21.72 DDN (DataDirect Networks)
  • 21.73 Decisyon
  • 21.74 Dell Technologies
  • 21.75 Deloitte
  • 21.76 Demandbase
  • 21.77 Denodo Technologies
  • 21.78 Dianomic Systems
  • 21.79 Digital Reasoning Systems
  • 21.80 Dimensional Insight
  • 21.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group
  • 21.82 Domino Data Lab
  • 21.83 Domo
  • 21.84 Dremio
  • 21.85 DriveScale
  • 21.86 Druva
  • 21.87 Dundas Data Visualization
  • 21.88 DXC Technology
  • 21.89 Elastic
  • 21.90 Engineering Group (Engineering Ingegneria Informatica)
  • 21.91 EnterpriseDB Corporation
  • 21.92 eQ Technologic
  • 21.93 Ericsson
  • 21.94 Erwin
  • 21.95 EVō (Big Cloud Analytics)
  • 21.96 EXASOL
  • 21.97 EXL (ExlService Holdings)
  • 21.98 Facebook
  • 21.99 FICO (Fair Isaac Corporation)
  • 21.100 Figure Eight
  • 21.101 FogHorn Systems
  • 21.102 Fractal Analytics
  • 21.103 Franz
  • 21.104 Fujitsu
  • 21.105 Fuzzy Logix
  • 21.106 Gainsight
  • 21.107 GE (General Electric)
  • 21.108 Glassbeam
  • 21.109 GoodData Corporation
  • 21.110 Google/Alphabet
  • 21.111 Grakn Labs
  • 21.112 Greenwave Systems
  • 21.113 GridGain Systems
  • 21.114 H2O.ai
  • 21.115 HarperDB
  • 21.116 Hedvig
  • 21.117 Hitachi Vantara
  • 21.118 Hortonworks
  • 21.119 HPE (Hewlett Packard Enterprise)
  • 21.120 Huawei
  • 21.121 HVR
  • 21.122 HyperScience
  • 21.123 HyTrust
  • 21.124 IBM Corporation
  • 21.125 iDashboards
  • 21.126 IDERA
  • 21.127 Ignite Technologies
  • 21.128 Imanis Data
  • 21.129 Impetus Technologies
  • 21.130 Incorta
  • 21.131 InetSoft Technology Corporation
  • 21.132 InfluxData
  • 21.133 Infogix
  • 21.134 Infor/Birst
  • 21.135 Informatica
  • 21.136 Information Builders
  • 21.137 Infosys
  • 21.138 Infoworks
  • 21.139 Insightsoftware.com
  • 21.140 InsightSquared
  • 21.141 Intel Corporation
  • 21.142 Interana
  • 21.143 InterSystems Corporation
  • 21.144 Jedox
  • 21.145 Jethro
  • 21.146 Jinfonet Software
  • 21.147 Juniper Networks
  • 21.148 KALEAO
  • 21.149 Keen IO
  • 21.150 Keyrus
  • 21.151 Kinetica
  • 21.152 KNIME
  • 21.153 Kognitio
  • 21.154 Kyvos Insights
  • 21.155 LeanXcale
  • 21.156 Lexalytics
  • 21.157 Lexmark International
  • 21.158 Lightbend
  • 21.159 Logi Analytics
  • 21.160 Logical Clocks
  • 21.161 Longview Solutions/Tidemark
  • 21.162 Looker Data Sciences
  • 21.163 LucidWorks
  • 21.164 Luminoso Technologies
  • 21.165 Maana
  • 21.166 Manthan Software Services
  • 21.167 MapD Technologies
  • 21.168 MapR Technologies
  • 21.169 MariaDB Corporation
  • 21.170 MarkLogic Corporation
  • 21.171 Mathworks
  • 21.172 Melissa
  • 21.173 MemSQL
  • 21.174 Metric Insights
  • 21.175 Microsoft Corporation
  • 21.176 MicroStrategy
  • 21.177 Minitab
  • 21.178 MongoDB
  • 21.179 Mu Sigma
  • 21.180 NEC Corporation
  • 21.181 Neo4j
  • 21.182 NetApp
  • 21.183 Nimbix
  • 21.184 Nokia
  • 21.185 NTT Data Corporation
  • 21.186 Numerify
  • 21.187 NuoDB
  • 21.188 NVIDIA Corporation
  • 21.189 Objectivity
  • 21.190 Oblong Industries
  • 21.191 OpenText Corporation
  • 21.192 Opera Solutions
  • 21.193 Optimal Plus
  • 21.194 Oracle Corporation
  • 21.195 Palantir Technologies
  • 21.196 Panasonic Corporation/Arimo
  • 21.197 Panorama Software
  • 21.198 Paxata
  • 21.199 Pepperdata
  • 21.200 Phocas Software
  • 21.201 Pivotal Software
  • 21.202 Prognoz
  • 21.203 Progress Software Corporation
  • 21.204 Provalis Research
  • 21.205 Pure Storage
  • 21.206 PwC (PricewaterhouseCoopers International)
  • 21.207 Pyramid Analytics
  • 21.208 Qlik
  • 21.209 Qrama/Tengu
  • 21.210 Quantum Corporation
  • 21.211 Qubole
  • 21.212 Rackspace
  • 21.213 Radius Intelligence
  • 21.214 RapidMiner
  • 21.215 Recorded Future
  • 21.216 Red Hat
  • 21.217 Redis Labs
  • 21.218 RedPoint Global
  • 21.219 Reltio
  • 21.220 RStudio
  • 21.221 Rubrik/Datos IO
  • 21.222 Ryft
  • 21.223 Sailthru
  • 21.224 Salesforce.com
  • 21.225 Salient Management Company
  • 21.226 Samsung Group
  • 21.227 SAP
  • 21.228 SAS Institute
  • 21.229 ScaleOut Software
  • 21.230 Seagate Technology
  • 21.231 Sinequa
  • 21.232 SiSense
  • 21.233 Sizmek
  • 21.234 SnapLogic
  • 21.235 Snowflake Computing
  • 21.236 Software AG
  • 21.237 Splice Machine
  • 21.238 Splunk
  • 21.239 Strategy Companion Corporation
  • 21.240 Stratio
  • 21.241 Streamlio
  • 21.242 StreamSets
  • 21.243 Striim
  • 21.244 Sumo Logic
  • 21.245 Supermicro (Super Micro Computer)
  • 21.246 Syncsort
  • 21.247 SynerScope
  • 21.248 SYNTASA
  • 21.249 Tableau Software
  • 21.250 Talend
  • 21.251 Tamr
  • 21.252 TARGIT
  • 21.253 TCS (Tata Consultancy Services)
  • 21.254 Teradata Corporation
  • 21.255 Thales/Guavus
  • 21.256 ThoughtSpot
  • 21.257 TIBCO Software
  • 21.258 Toshiba Corporation
  • 21.259 Transwarp
  • 21.260 Trifacta
  • 21.261 Unifi Software
  • 21.262 Unravel Data
  • 21.263 VANTIQ
  • 21.264 VMware
  • 21.265 VoltDB
  • 21.266 WANdisco
  • 21.267 Waterline Data
  • 21.268 Western Digital Corporation
  • 21.269 WhereScape
  • 21.270 WiPro
  • 21.271 Wolfram Research
  • 21.272 Workday
  • 21.273 Xplenty
  • 21.274 Yellowfin BI
  • 21.275 Yseop
  • 21.276 Zendesk
  • 21.277 Zoomdata
  • 21.278 Zucchetti

22 Chapter 22: Conclusion & Strategic Recommendations

  • 22.1 Why is the Market Poised to Grow?
  • 22.2 Moving Towards Consolidation: Review of M&A Activity in the Vendor Arena
  • 22.3 Maturation of AI (Artificial Intelligence): From Machine Learning to Deep Learning
  • 22.4 Blockchain: Impact on Big Data
  • 22.5 The Emergence of Edge Analytics
  • 22.6 Beyond Data Capture & Analytics
  • 22.7 Transforming IT from a Cost Center to a Profit Center
  • 22.8 Can Privacy Implications Hinder Success?
  • 22.9 Maximizing Innovation with Careful Regulation
  • 22.10 Battling Organizational & Data Silos
  • 22.11 Moving Big Data to the Cloud
  • 22.12 Software vs. Hardware Investments
  • 22.13 Vendor Share: Who Leads the Market?
  • 22.14 Big Data Driving Wider IT Industry Investments
  • 22.15 Assessing the Impact of the IoT
  • 22.16 Recommendations
    • 22.16.1 Big Data Hardware, Software & Professional Services Providers
    • 22.16.2 Enterprises

List of Figures

  • Figure 1: Hadoop Architecture
  • Figure 2: Reactive vs. Proactive Analytics
  • Figure 3: Big Data Future Roadmap: 2018 - 2030
  • Figure 4: Big Data Value Chain
  • Figure 5: Key Aspects of Big Data Standardization
  • Figure 6: Global Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 7: Global Big Data Revenue by Submarket: 2018 - 2030 ($ Million)
  • Figure 8: Global Big Data Storage and Compute Infrastructure Submarket Revenue: 2018 - 2030 ($ Million)
  • Figure 9: Global Big Data Networking Infrastructure Submarket Revenue: 2018 - 2030 ($ Million)
  • Figure 10: Global Big Data Hadoop & Infrastructure Software Submarket Revenue: 2018 - 2030 ($ Million)
  • Figure 11: Global Big Data SQL Submarket Revenue: 2018 - 2030 ($ Million)
  • Figure 12: Global Big Data NoSQL Submarket Revenue: 2018 - 2030 ($ Million)
  • Figure 13: Global Big Data Analytic Platforms & Applications Submarket Revenue: 2018 - 2030 ($ Million)
  • Figure 14: Global Big Data Cloud Platforms Submarket Revenue: 2018 - 2030 ($ Million)
  • Figure 15: Global Big Data Professional Services Submarket Revenue: 2018 - 2030 ($ Million)
  • Figure 16: Global Big Data Revenue by Vertical Market: 2018 - 2030 ($ Million)
  • Figure 17: Global Big Data Revenue in the Automotive, Aerospace & Transportation Sector: 2018 - 2030 ($ Million)
  • Figure 18: Global Big Data Revenue in the Banking & Securities Sector: 2018 - 2030 ($ Million)
  • Figure 19: Global Big Data Revenue in the Defense & Intelligence Sector: 2018 - 2030 ($ Million)
  • Figure 20: Global Big Data Revenue in the Education Sector: 2018 - 2030 ($ Million)
  • Figure 21: Global Big Data Revenue in the Healthcare & Pharmaceutical Sector: 2018 - 2030 ($ Million)
  • Figure 22: Global Big Data Revenue in the Smart Cities & Intelligent Buildings Sector: 2018 - 2030 ($ Million)
  • Figure 23: Global Big Data Revenue in the Insurance Sector: 2018 - 2030 ($ Million)
  • Figure 24: Global Big Data Revenue in the Manufacturing & Natural Resources Sector: 2018 - 2030 ($ Million)
  • Figure 25: Global Big Data Revenue in the Media & Entertainment Sector: 2018 - 2030 ($ Million)
  • Figure 26: Global Big Data Revenue in the Public Safety & Homeland Security Sector: 2018 - 2030 ($ Million)
  • Figure 27: Global Big Data Revenue in the Public Services Sector: 2018 - 2030 ($ Million)
  • Figure 28: Global Big Data Revenue in the Retail, Wholesale & Hospitality Sector: 2018 - 2030 ($ Million)
  • Figure 29: Global Big Data Revenue in the Telecommunications Sector: 2018 - 2030 ($ Million)
  • Figure 30: Global Big Data Revenue in the Utilities & Energy Sector: 2018 - 2030 ($ Million)
  • Figure 31: Global Big Data Revenue in Other Vertical Sectors: 2018 - 2030 ($ Million)
  • Figure 32: Big Data Revenue by Region: 2018 - 2030 ($ Million)
  • Figure 33: Asia Pacific Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 34: Asia Pacific Big Data Revenue by Country: 2018 - 2030 ($ Million)
  • Figure 35: Australia Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 36: China Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 37: India Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 38: Indonesia Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 39: Japan Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 40: Malaysia Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 41: Pakistan Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 42: Philippines Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 43: Singapore Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 44: South Korea Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 45: Taiwan Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 46: Thailand Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 47: Big Data Revenue in the Rest of Asia Pacific: 2018 - 2030 ($ Million)
  • Figure 48: Eastern Europe Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 49: Eastern Europe Big Data Revenue by Country: 2018 - 2030 ($ Million)
  • Figure 50: Czech Republic Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 51: Poland Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 52: Russia Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 53: Big Data Revenue in the Rest of Eastern Europe: 2018 - 2030 ($ Million)
  • Figure 54: Latin & Central America Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 55: Latin & Central America Big Data Revenue by Country: 2018 - 2030 ($ Million)
  • Figure 56: Argentina Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 57: Brazil Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 58: Mexico Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 59: Big Data Revenue in the Rest of Latin & Central America: 2018 - 2030 ($ Million)
  • Figure 60: Middle East & Africa Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 61: Middle East & Africa Big Data Revenue by Country: 2018 - 2030 ($ Million)
  • Figure 62: Israel Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 63: Qatar Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 64: Saudi Arabia Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 65: South Africa Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 66: UAE Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 67: Big Data Revenue in the Rest of the Middle East & Africa: 2018 - 2030 ($ Million)
  • Figure 68: North America Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 69: North America Big Data Revenue by Country: 2018 - 2030 ($ Million)
  • Figure 70: Canada Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 71: USA Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 72: Western Europe Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 73: Western Europe Big Data Revenue by Country: 2018 - 2030 ($ Million)
  • Figure 74: Denmark Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 75: Finland Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 76: France Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 77: Germany Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 78: Italy Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 79: Netherlands Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 80: Norway Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 81: Spain Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 82: Sweden Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 83: UK Big Data Revenue: 2018 - 2030 ($ Million)
  • Figure 84: Big Data Revenue in the Rest of Western Europe: 2018 - 2030 ($ Million)
  • Figure 85: Global Big Data Workload Distribution by Environment: 2018 - 2030 (%)
  • Figure 86: Global Big Data Revenue by Hardware, Software & Professional Services: 2018 - 2030 ($ Million)
  • Figure 87: Big Data Vendor Market Share: 2017 (%)
  • Figure 88: Global IT Expenditure Driven by Big Data Investments: 2018 - 2030 ($ Million)
  • Figure 89: Global IoT Connections by Access Technology: 2018 - 2030 (Millions)
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