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自動車産業のビッグデータ:機会、課題、戦略、予測

Big Data in the Automotive Industry: 2018 - 2030 - Opportunities, Challenges, Strategies & Forecasts

発行 SNS Telecom & IT 商品コード 498148
出版日 ページ情報 英文 501 Pages
納期: 即日から翌営業日
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自動車産業のビッグデータ:機会、課題、戦略、予測 Big Data in the Automotive Industry: 2018 - 2030 - Opportunities, Challenges, Strategies & Forecasts
出版日: 2018年07月14日 ページ情報: 英文 501 Pages
概要

当レポートでは、自動車産業のビッグデータについて調査分析し、市場促進要因、課題、投資の可能性、アプリケーション分野、ユースケース、将来のロードマップ、バリューチェーン、ケーススタディ、ベンダーのプロファイル、戦略について、体系的な情報を提供しています。

第1章 イントロダクション

第2章 ビッグデータの概要

  • ビッグデータとは
  • ビッグデータプロセッシングへの主なアプローチ
  • ビッグデータの主な特徴
  • 市場成長促進要因
  • 市場の障壁

第3章 ビッグデータアナリティクス

  • ビッグデータアナリティクスとは
  • アナリティクスの重要性
  • リアクティブ vs. プロアクティブアナリティクス
  • 顧客 vs. オペレーショナルアナリティクス
  • 技術・実装のアプローチ

第4章 自動車産業におけるビジネスケースと用途

  • 概要と投資の潜在性
  • 産業固有の市場成長促進要因
  • 産業固有の市場の障壁
  • 主な用途

第5章 自動車産業のケーススタディ

  • 自動車OEM
  • その他のステークホルダー

第6章 将来のロードマップとバリューチェーン

  • 将来のロードマップ
  • バリューチェーン

第7章 標準化と規制のイニシアチブ

  • ASF (Apache Software Foundation)
  • CSA (Cloud Security Alliance)
  • CSCC (Cloud Standards Customer Council)
  • DMG (Data Mining Group)
  • IEEE (Institute of Electrical and Electronics Engineers)
  • INCITS (InterNational Committee for Information Technology Standards)
  • ISO (International Organization for Standardization)
  • ITU (International Telecommunications Union)
  • Linux Foundation
  • NIST (National Institute of Standards and Technology)
  • OASIS (Organization for the Advancement of Structured Information Standards)
  • ODaF (Open Data Foundation)
  • ODCA (Open Data Center Alliance)
  • OGC (Open Geospatial Consortium)
  • TM Forum
  • TPC (Transaction Processing Performance Council)
  • W3C (World Wide Web Consortium)

第8章 市場の分析と予測

  • 世界の自動車産業におけるビッグデータの見通し
  • 市場区分
  • 水平的サブマーケット
  • ハードウェア
  • ソフトウェア
  • プロフェッショナルサービス
  • アプリケーション分野
  • ユースケース
  • 製品開発・製造・サプライチェーン
  • アフターセールス、保証、ディーラー管理
  • コネクテッドカー、ITS
  • マーケティング、販売、その他
  • 地域別の見通し
  • アジア太平洋地域
  • 東欧
  • ラテンアメリカ・中米
  • 中東・アフリカ
  • 北米
  • 西欧

第9章 ベンダー情勢

  • 1010data
  • Absolutdata
  • Accenture
  • Actian Corporation
  • Adaptive Insights
  • Advizor Solutions
  • AeroSpike
  • AFS Technologies
  • Alation
  • Algorithmia
  • Alluxio
  • Alpine Data
  • Alteryx
  • AMD (Advanced Micro Devices)
  • Apixio
  • Arcadia Data
  • Arimo
  • ARM
  • AtScale
  • Attivio、など

第10章 結論と戦略的提言

目次

“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 and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The automotive industry is no exception to this trend, where Big Data has found a host of applications ranging from product design and manufacturing to predictive vehicle maintenance and autonomous driving.

SNS Telecom & IT estimates that Big Data investments in the automotive industry will account for more than $3.3 Billion in 2018 alone. Led by a plethora of business opportunities for automotive OEMs, tier-1 suppliers, insurers, dealerships and other stakeholders, these investments are further expected to grow at a CAGR of approximately 16% over the next three years.

The “Big Data in the Automotive Industry: 2018 - 2030 - Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the automotive industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2018 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 4 application areas, 18 use cases, 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.

Table of Contents

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

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

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

Chapter 4: Business Case & Applications in the Automotive Industry

  • 4.1. Overview & Investment Potential
  • 4.2. Industry Specific Market Growth Drivers
  • 4.3. Industry Specific Market Barriers
  • 4.4. Key Applications
    • 4.4.1. Product Development, Manufacturing & Supply Chain
      • 4.4.1.1. Optimizing the Supply Chain
      • 4.4.1.2. Eliminating Manufacturing Defects
      • 4.4.1.3. Customer-Driven Product Design & Planning
    • 4.4.2. After-Sales, Warranty & Dealer Management
      • 4.4.2.1. Predictive Maintenance & Real-Time Diagnostics
      • 4.4.2.2. Streamlining Recalls & Warranty
      • 4.4.2.3. Parts Inventory & Pricing Optimization
      • 4.4.2.4. Dealer Management & Customer Support Services
    • 4.4.3. Connected Vehicles & Intelligent Transportation
      • 4.4.3.1. UBI (Usage-Based Insurance)
      • 4.4.3.2. Autonomous & Semi-Autonomous Driving
      • 4.4.3.3. Intelligent Transportation
      • 4.4.3.4. Fleet Management
      • 4.4.3.5. Driver Safety & Vehicle Cyber Security
      • 4.4.3.6. In-Vehicle Experience, Navigation & Infotainment
      • 4.4.3.7. Ride Sourcing, Sharing & Rentals
    • 4.4.4. Marketing, Sales & Other Applications
      • 4.4.4.1. Marketing & Sales
      • 4.4.4.2. Customer Retention
      • 4.4.4.3. Third Party Monetization
      • 4.4.4.4. Other Applications

Chapter 5: Automotive Industry Case Studies

  • 5.1. Automotive OEMs
    • 5.1.1. Audi: Facilitating Efficient Production Processes with Big Data
    • 5.1.2. BMW: Eliminating Defects in New Vehicle Models with Big Data
    • 5.1.3. Daimler: Ensuring Quality Assurance with Big Data
    • 5.1.4. Dongfeng Motor Corporation: Enriching Network-Connected Autonomous Vehicles with Big Data
    • 5.1.5. FCA (Fiat Chrysler Automobiles): Enhancing Dealer Management with Big Data
    • 5.1.6. Ford Motor Company: Making Efficient Transportation Decisions with Big Data
    • 5.1.7. GM (General Motors Company): Personalizing In-Vehicle Experience with Big Data
    • 5.1.8. Groupe PSA: Reducing Industrial Energy Bills with Big Data
    • 5.1.9. Groupe Renault: Boosting Driver Safety with Big Data
    • 5.1.10. Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data
    • 5.1.11. Hyundai Motor Company: Empowering Connected & Self-Driving Cars with Big Data
    • 5.1.12. Jaguar Land Rover: Realizing Better & Cheaper Vehicle Designs with Big Data
    • 5.1.13. Mazda Motor Corporation: Creating Better Engines with Big Data
    • 5.1.14. Nissan Motor Company: Leveraging Big Data to Drive After-Sales Business Growth
    • 5.1.15. SAIC Motor Corporation: Transforming Stressful Driving to Enjoyable Moments with Big Data
    • 5.1.16. Subaru: Turbocharging Dealer Interaction with Big Data
    • 5.1.17. Suzuki Motor Corporation: Accelerating Vehicle Design and Innovation with Big Data
    • 5.1.18. Tesla: Achieving Customer Loyalty with Big Data
    • 5.1.19. Toyota Motor Corporation: Powering Smart Cars with Big Data
    • 5.1.20. Volkswagen Group: Transitioning to End-to-End Mobility Solutions with Big Data
    • 5.1.21. Volvo Cars: Reducing Breakdowns and Failures with Big Data
  • 5.2. Other Stakeholders
    • 5.2.1. Allstate Corporation & Arity: Making Transportation Safer & Smarter with Big Data
    • 5.2.2. automotiveMastermind: Helping Automotive Dealerships Increase Sales with Big Data
    • 5.2.3. Continental: Making Vehicles Safer with Big Data
    • 5.2.4. Cox Automotive: Transforming the Used Vehicle Lifecycle with Big Data
    • 5.2.5. Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data
    • 5.2.6. Delphi Automotive: Monetizing Connected Vehicles with Big Data
    • 5.2.7. Denso Corporation: Enabling Hazard Prediction with Big Data
    • 5.2.8. HERE: Easing Traffic Congestion with Big Data
    • 5.2.9. Lytx: Ensuring Road Safety with Big Data
    • 5.2.10. Michelin: Optimizing Tire Manufacturing with Big Data
    • 5.2.11. Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data
    • 5.2.12. Bosch: Empowering Fleet Management & Vehicle Insurance with Big Data
    • 5.2.13. THTA (Tokyo Hire-Taxi Association): Making Connected Taxis a Reality with Big Data
    • 5.2.14. Uber Technologies: Revolutionizing Ride Sourcing with Big Data
    • 5.2.15. U.S. Xpress: Driving Fuel-Savings with Big Data

Chapter 6: Future Roadmap & Value Chain

  • 6.1. Future Roadmap
    • 6.1.1. Pre-2020: Investments in Advanced Analytics for Vehicle-Related Services
    • 6.1.2. 2020 - 2025: Proliferation of Real-Time Edge Analytics & Automotive Data Monetization
    • 6.1.3. 2025 - 2030: Towards Fully Autonomous Driving & Future IoT Applications
  • 6.2. The Big Data Value Chain
    • 6.2.1. Hardware Providers
      • 6.2.1.1. Storage & Compute Infrastructure Providers
      • 6.2.1.2. Networking Infrastructure Providers
    • 6.2.2. Software Providers
      • 6.2.2.1. Hadoop & Infrastructure Software Providers
      • 6.2.2.2. SQL & NoSQL Providers
      • 6.2.2.3. Analytic Platform & Application Software Providers
      • 6.2.2.4. Cloud Platform Providers
    • 6.2.3. Professional Services Providers
    • 6.2.4. End-to-End Solution Providers
    • 6.2.5. Automotive Industry

Chapter 7: Standardization & Regulatory Initiatives

  • 7.1. ASF (Apache Software Foundation)
    • 7.1.1. Management of Hadoop
    • 7.1.2. Big Data Projects Beyond Hadoop
  • 7.2. CSA (Cloud Security Alliance)
    • 7.2.1. BDWG (Big Data Working Group)
  • 7.3. CSCC (Cloud Standards Customer Council)
    • 7.3.1. Big Data Working Group
  • 7.4. DMG (Data Mining Group)
    • 7.4.1. PMML (Predictive Model Markup Language) Working Group
    • 7.4.2. PFA (Portable Format for Analytics) Working Group
  • 7.5. IEEE (Institute of Electrical and Electronics Engineers)
    • 7.5.1. Big Data Initiative
  • 7.6. INCITS (InterNational Committee for Information Technology Standards)
    • 7.6.1. Big Data Technical Committee
  • 7.7. ISO (International Organization for Standardization)
    • 7.7.1. ISO/IEC JTC 1/SC 32: Data Management and Interchange
    • 7.7.2. ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
    • 7.7.3. ISO/IEC JTC 1/SC 27: IT Security Techniques
    • 7.7.4. ISO/IEC JTC 1/WG 9: Big Data
    • 7.7.5. Collaborations with Other ISO Work Groups
  • 7.8. ITU (International Telecommunication Union)
    • 7.8.1. ITU-T Y.3600: Big Data - Cloud Computing Based Requirements and Capabilities
    • 7.8.2. Other Deliverables Through SG (Study Group) 13 on Future Networks
    • 7.8.3. Other Relevant Work
  • 7.9. Linux Foundation
    • 7.9.1. ODPi (Open Ecosystem of Big Data)
  • 7.10. NIST (National Institute of Standards and Technology)
    • 7.10.1. NBD-PWG (NIST Big Data Public Working Group)
  • 7.11. OASIS (Organization for the Advancement of Structured Information Standards)
    • 7.11.1. Technical Committees
  • 7.12. ODaF (Open Data Foundation)
    • 7.12.1. Big Data Accessibility
  • 7.13. ODCA (Open Data Center Alliance)
    • 7.13.1. Work on Big Data
  • 7.14. OGC (Open Geospatial Consortium)
    • 7.14.1. Big Data DWG (Domain Working Group)
  • 7.15. TM Forum
    • 7.15.1. Big Data Analytics Strategic Program
  • 7.16. TPC (Transaction Processing Performance Council)
    • 7.16.1. TPC-BDWG (TPC Big Data Working Group)
  • 7.17. W3C (World Wide Web Consortium)
    • 7.17.1. Big Data Community Group
    • 7.17.2. Open Government Community Group

Chapter 8: Market Sizing & Forecasts

  • 8.1. Global Outlook for Big Data in the Automotive Industry
  • 8.2. Hardware, Software & Professional Services Segmentation
  • 8.3. Horizontal Submarket Segmentation
  • 8.4. Hardware Submarkets
    • 8.4.1. Storage and Compute Infrastructure
    • 8.4.2. Networking Infrastructure
  • 8.5. Software Submarkets
    • 8.5.1. Hadoop & Infrastructure Software
    • 8.5.2. SQL
    • 8.5.3. NoSQL
    • 8.5.4. Analytic Platforms & Applications
    • 8.5.5. Cloud Platforms
  • 8.6. Professional Services Submarket
    • 8.6.1. Professional Services
  • 8.7. Application Area Segmentation
    • 8.7.1. Product Development, Manufacturing & Supply Chain
    • 8.7.2. After-Sales, Warranty & Dealer Management
    • 8.7.3. Connected Vehicles & Intelligent Transportation
    • 8.7.4. Marketing, Sales & Other Applications
  • 8.8. Use Case Segmentation
  • 8.9. Product Development, Manufacturing & Supply Chain Use Cases
    • 8.9.1. Supply Chain Management
    • 8.9.2. Manufacturing
    • 8.9.3. Product Design & Planning
  • 8.10. After-Sales, Warranty & Dealer Management Use Cases
    • 8.10.1. Predictive Maintenance & Real-Time Diagnostics
    • 8.10.2. Recall & Warranty Management
    • 8.10.3. Parts Inventory & Pricing Optimization
    • 8.10.4. Dealer Management & Customer Support Services
  • 8.11. Connected Vehicles & Intelligent Transportation Use Cases
    • 8.11.1. UBI (Usage-Based Insurance)
    • 8.11.2. Autonomous & Semi-Autonomous Driving
    • 8.11.3. Intelligent Transportation
    • 8.11.4. Fleet Management
    • 8.11.5. Driver Safety & Vehicle Cyber Security
    • 8.11.6. In-Vehicle Experience, Navigation & Infotainment
    • 8.11.7. Ride Sourcing, Sharing & Rentals
  • 8.12. Marketing, Sales & Other Application Use Cases
    • 8.12.1. Marketing & Sales
    • 8.12.2. Customer Retention
    • 8.12.3. Third Party Monetization
    • 8.12.4. Other Use Cases
  • 8.13. Regional Outlook
  • 8.14. Asia Pacific
    • 8.14.1. Country Level Segmentation
    • 8.14.2. Australia
    • 8.14.3. China
    • 8.14.4. India
    • 8.14.5. Indonesia
    • 8.14.6. Japan
    • 8.14.7. Malaysia
    • 8.14.8. Pakistan
    • 8.14.9. Philippines
    • 8.14.10. Singapore
    • 8.14.11. South Korea
    • 8.14.12. Taiwan
    • 8.14.13. Thailand
    • 8.14.14. Rest of Asia Pacific
  • 8.15. Eastern Europe
    • 8.15.1. Country Level Segmentation
    • 8.15.2. Czech Republic
    • 8.15.3. Poland
    • 8.15.4. Russia
    • 8.15.5. Rest of Eastern Europe
  • 8.16. Latin & Central America
    • 8.16.1. Country Level Segmentation
    • 8.16.2. Argentina
    • 8.16.3. Brazil
    • 8.16.4. Mexico
    • 8.16.5. Rest of Latin & Central America
  • 8.17. Middle East & Africa
    • 8.17.1. Country Level Segmentation
    • 8.17.2. Israel
    • 8.17.3. Qatar
    • 8.17.4. Saudi Arabia
    • 8.17.5. South Africa
    • 8.17.6. UAE
    • 8.17.7. Rest of the Middle East & Africa
  • 8.18. North America
    • 8.18.1. Country Level Segmentation
    • 8.18.2. Canada
    • 8.18.3. USA
  • 8.19. Western Europe
    • 8.19.1. Country Level Segmentation
    • 8.19.2. Denmark
    • 8.19.3. Finland
    • 8.19.4. France
    • 8.19.5. Germany
    • 8.19.6. Italy
    • 8.19.7. Netherlands
    • 8.19.8. Norway
    • 8.19.9. Spain
    • 8.19.10. Sweden
    • 8.19.11. UK
    • 8.19.12. Rest of Western Europe

Chapter 9: Vendor Landscape

  • 9.1. 1010data
  • 9.2. Absolutdata
  • 9.3. Accenture
  • 9.4. Actian Corporation/HCL Technologies
  • 9.5. Adaptive Insights
  • 9.6. Adobe Systems
  • 9.7. Advizor Solutions
  • 9.8. AeroSpike
  • 9.9. AFS Technologies
  • 9.10. Alation
  • 9.11. Algorithmia
  • 9.12. Alluxio
  • 9.13. ALTEN
  • 9.14. Alteryx
  • 9.15. AMD (Advanced Micro Devices)
  • 9.16. Anaconda
  • 9.17. Apixio
  • 9.18. Arcadia Data
  • 9.19. ARM
  • 9.20. AtScale
  • 9.21. Attivio
  • 9.22. Attunity
  • 9.23. Automated Insights
  • 9.24. AVORA
  • 9.25. AWS (Amazon Web Services)
  • 9.26. Axiomatics
  • 9.27. Ayasdi
  • 9.28. BackOffice Associates
  • 9.29. Basho Technologies
  • 9.30. BCG (Boston Consulting Group)
  • 9.31. Bedrock Data
  • 9.32. BetterWorks
  • 9.33. Big Panda
  • 9.34. BigML
  • 9.35. Bitam
  • 9.36. Blue Medora
  • 9.37. BlueData Software
  • 9.38. BlueTalon
  • 9.39. BMC Software
  • 9.40. BOARD International
  • 9.41. Booz Allen Hamilton
  • 9.42. Boxever
  • 9.43. CACI International
  • 9.44. Cambridge Semantics
  • 9.45. Capgemini
  • 9.46. Cazena
  • 9.47. Centrifuge Systems
  • 9.48. CenturyLink
  • 9.49. Chartio
  • 9.50. Cisco Systems
  • 9.51. Civis Analytics
  • 9.52. ClearStory Data
  • 9.53. Cloudability
  • 9.54. Cloudera
  • 9.55. Cloudian
  • 9.56. Clustrix
  • 9.57. CognitiveScale
  • 9.58. Collibra
  • 9.59. Concurrent Technology/Vecima Networks
  • 9.60. Confluent
  • 9.61. Contexti
  • 9.62. Couchbase
  • 9.63. Crate.io
  • 9.64. Cray
  • 9.65. Databricks
  • 9.66. Dataiku
  • 9.67. Datalytyx
  • 9.68. Datameer
  • 9.69. DataRobot
  • 9.70. DataStax
  • 9.71. Datawatch Corporation
  • 9.72. DDN (DataDirect Networks)
  • 9.73. Decisyon
  • 9.74. Dell Technologies
  • 9.75. Deloitte
  • 9.76. Demandbase
  • 9.77. Denodo Technologies
  • 9.78. Dianomic Systems
  • 9.79. Digital Reasoning Systems
  • 9.80. Dimensional Insight
  • 9.81. Dolphin Enterprise Solutions Corporation/Hanse Orga Group
  • 9.82. Domino Data Lab
  • 9.83. Domo
  • 9.84. Dremio
  • 9.85. DriveScale
  • 9.86. Druva
  • 9.87. Dundas Data Visualization
  • 9.88. DXC Technology
  • 9.89. Elastic
  • 9.90. Engineering Group (Engineering Ingegneria Informatica)
  • 9.91. EnterpriseDB Corporation
  • 9.92. eQ Technologic
  • 9.93. Ericsson
  • 9.94. Erwin
  • 9.95. EVO (Big Cloud Analytics)
  • 9.96. EXASOL
  • 9.97. EXL (ExlService Holdings)
  • 9.98. Facebook
  • 9.99. FICO (Fair Isaac Corporation)
  • 9.100. Figure Eight
  • 9.101. FogHorn Systems
  • 9.102. Fractal Analytics
  • 9.103. Franz
  • 9.104. Fujitsu
  • 9.105. Fuzzy Logix
  • 9.106. Gainsight
  • 9.107. GE (General Electric)
  • 9.108. Glassbeam
  • 9.109. GoodData Corporation
  • 9.110. Google/Alphabet
  • 9.111. Grakn Labs
  • 9.112. Greenwave Systems
  • 9.113. GridGain Systems
  • 9.114. H2O.ai
  • 9.115. HarperDB
  • 9.116. Hedvig
  • 9.117. Hitachi Vantara
  • 9.118. Hortonworks
  • 9.119. HPE (Hewlett Packard Enterprise)
  • 9.120. Huawei
  • 9.121. HVR
  • 9.122. HyperScience
  • 9.123. HyTrust
  • 9.124. IBM Corporation
  • 9.125. iDashboards
  • 9.126. IDERA
  • 9.127. Ignite Technologies
  • 9.128. Imanis Data
  • 9.129. Impetus Technologies
  • 9.130. Incorta
  • 9.131. InetSoft Technology Corporation
  • 9.132. InfluxData
  • 9.133. Infogix
  • 9.134. Infor/Birst
  • 9.135. Informatica
  • 9.136. Information Builders
  • 9.137. Infosys
  • 9.138. Infoworks
  • 9.139. Insightsoftware.com
  • 9.140. InsightSquared
  • 9.141. Intel Corporation
  • 9.142. Interana
  • 9.143. InterSystems Corporation
  • 9.144. Jedox
  • 9.145. Jethro
  • 9.146. Jinfonet Software
  • 9.147. Juniper Networks
  • 9.148. KALEAO
  • 9.149. Keen IO
  • 9.150. Keyrus
  • 9.151. Kinetica
  • 9.152. KNIME
  • 9.153. Kognitio
  • 9.154. Kyvos Insights
  • 9.155. LeanXcale
  • 9.156. Lexalytics
  • 9.157. Lexmark International
  • 9.158. Lightbend
  • 9.159. Logi Analytics
  • 9.160. Logical Clocks
  • 9.161. Longview Solutions/Tidemark
  • 9.162. Looker Data Sciences
  • 9.163. LucidWorks
  • 9.164. Luminoso Technologies
  • 9.165. Maana
  • 9.166. Manthan Software Services
  • 9.167. MapD Technologies
  • 9.168. MapR Technologies
  • 9.169. MariaDB Corporation
  • 9.170. MarkLogic Corporation
  • 9.171. Mathworks
  • 9.172. Melissa
  • 9.173. MemSQL
  • 9.174. Metric Insights
  • 9.175. Microsoft Corporation
  • 9.176. MicroStrategy
  • 9.177. Minitab
  • 9.178. MongoDB
  • 9.179. Mu Sigma
  • 9.180. NEC Corporation
  • 9.181. Neo4j
  • 9.182. NetApp
  • 9.183. Nimbix
  • 9.184. Nokia
  • 9.185. NTT Data Corporation
  • 9.186. Numerify
  • 9.187. NuoDB
  • 9.188. NVIDIA Corporation
  • 9.189. Objectivity
  • 9.190. Oblong Industries
  • 9.191. OpenText Corporation
  • 9.192. Opera Solutions
  • 9.193. Optimal Plus
  • 9.194. Oracle Corporation
  • 9.195. Palantir Technologies
  • 9.196. Panasonic Corporation/Arimo
  • 9.197. Panorama Software
  • 9.198. Paxata
  • 9.199. Pepperdata
  • 9.200. Phocas Software
  • 9.201. Pivotal Software
  • 9.202. Prognoz
  • 9.203. Progress Software Corporation
  • 9.204. Provalis Research
  • 9.205. Pure Storage
  • 9.206. PwC (PricewaterhouseCoopers International)
  • 9.207. Pyramid Analytics
  • 9.208. Qlik
  • 9.209. Qrama/Tengu
  • 9.210. Quantum Corporation
  • 9.211. Qubole
  • 9.212. Rackspace
  • 9.213. Radius Intelligence
  • 9.214. RapidMiner
  • 9.215. Recorded Future
  • 9.216. Red Hat
  • 9.217. Redis Labs
  • 9.218. RedPoint Global
  • 9.219. Reltio
  • 9.220. RStudio
  • 9.221. Rubrik/Datos IO
  • 9.222. Ryft
  • 9.223. Sailthru
  • 9.224. Salesforce.com
  • 9.225. Salient Management Company
  • 9.226. Samsung Group
  • 9.227. SAP
  • 9.228. SAS Institute
  • 9.229. ScaleOut Software
  • 9.230. Seagate Technology
  • 9.231. Sinequa
  • 9.232. SiSense
  • 9.233. Sizmek
  • 9.234. SnapLogic
  • 9.235. Snowflake Computing
  • 9.236. Software AG
  • 9.237. Splice Machine
  • 9.238. Splunk
  • 9.239. Strategy Companion Corporation
  • 9.240. Stratio
  • 9.241. Streamlio
  • 9.242. StreamSets
  • 9.243. Striim
  • 9.244. Sumo Logic
  • 9.245. Supermicro (Super Micro Computer)
  • 9.246. Syncsort
  • 9.247. SynerScope
  • 9.248. SYNTASA
  • 9.249. Tableau Software
  • 9.250. Talend
  • 9.251. Tamr
  • 9.252. TARGIT
  • 9.253. TCS (Tata Consultancy Services)
  • 9.254. Teradata Corporation
  • 9.255. Thales/Guavus
  • 9.256. ThoughtSpot
  • 9.257. TIBCO Software
  • 9.258. Toshiba Corporation
  • 9.259. Transwarp
  • 9.260. Trifacta
  • 9.261. Unifi Software
  • 9.262. Unravel Data
  • 9.263. VANTIQ
  • 9.264. VMware
  • 9.265. VoltDB
  • 9.266. WANdisco
  • 9.267. Waterline Data
  • 9.268. Western Digital Corporation
  • 9.269. WhereScape
  • 9.270. WiPro
  • 9.271. Wolfram Research
  • 9.272. Workday
  • 9.273. Xplenty
  • 9.274. Yellowfin BI
  • 9.275. Yseop
  • 9.276. Zendesk
  • 9.277. Zoomdata
  • 9.278. Zucchetti

Chapter 10: Conclusion & Strategic Recommendations

  • 10.1. Why is the Market Poised to Grow?
  • 10.2. Geographic Outlook: Which Countries Offer the Highest Growth Potential?
  • 10.3. Partnerships & M&A Activity: Highlighting the Importance of Big Data
  • 10.4. The Significance of Edge Analytics for Automotive Applications
  • 10.5. Achieving Customer Retention with Data-Driven Services
  • 10.6. Addressing Privacy Concerns
  • 10.7. The Role of Legislation
  • 10.8. Encouraging Data Sharing in the Automotive Industry
  • 10.9. Assessing the Impact of Self-Driving Vehicles
  • 10.10. Recommendations
    • 10.10.1. Big Data Hardware, Software & Professional Services Providers
    • 10.10.2. Automotive OEMS & Other Stakeholders

List of Figures

  • Figure 1: Hadoop Architecture
  • Figure 2: Reactive vs. Proactive Analytics
  • Figure 3: Distribution of Big Data Investments in the Automotive Industry, by Application Area: 2018 (%)
  • Figure 4: Autonomous Vehicle Generated Data Volume by Sensor (%)
  • Figure 5: On-Board Sensors in an Autonomous Vehicle
  • Figure 6: Audi's Enterprise Big Data Platform
  • Figure 7: Toyota's Smart Center Architecture
  • Figure 8: Progressive Corporation's Use of Big Data for Automotive Insurance
  • Figure 9: Big Data Roadmap in the Automotive Industry: 2018 - 2030
  • Figure 10: Big Data Value Chain in the Automotive Industry
  • Figure 11: Key Aspects of Big Data Standardization
  • Figure 12: Global Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 13: Global Big Data Revenue in the Automotive Industry, by Hardware, Software & Professional Services: 2018 - 2030 ($ Million)
  • Figure 14: Global Big Data Revenue in the Automotive Industry, by Submarket: 2018 - 2030 ($ Million)
  • Figure 15: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 16: Global Big Data Networking Infrastructure Submarket Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 17: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 18: Global Big Data SQL Submarket Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 19: Global Big Data NoSQL Submarket Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 20: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 21: Global Big Data Cloud Platforms Submarket Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 22: Global Big Data Professional Services Submarket Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 23: Global Big Data Revenue in the Automotive Industry, by Application Area: 2018 - 2030 ($ Million)
  • Figure 24: Global Big Data Revenue in Automotive Product Development, Manufacturing & Supply Chain: 2018 - 2030 ($ Million)
  • Figure 25: Global Big Data Revenue in Automotive After-Sales, Warranty & Dealer Management: 2018 - 2030 ($ Million)
  • Figure 26: Global Big Data Revenue in Connected Vehicles & Intelligent Transportation: 2018 - 2030 ($ Million)
  • Figure 27: Global Big Data Revenue in Automotive Marketing, Sales & Other Applications: 2018 - 2030 ($ Million)
  • Figure 28: Global Big Data Revenue in the Automotive Industry, by Use Case: 2018 - 2030 ($ Million)
  • Figure 29: Global Big Data Revenue in Automotive Supply Chain Management: 2018 - 2030 ($ Million)
  • Figure 30: Global Big Data Revenue in Automotive Manufacturing: 2018 - 2030 ($ Million)
  • Figure 31: Global Big Data Revenue in Automotive Product Design & Planning: 2018 - 2030 ($ Million)
  • Figure 32: Global Big Data Revenue in Automotive Predictive Maintenance & Real-Time Diagnostics: 2018 - 2030 ($ Million)
  • Figure 33: Global Big Data Revenue in Automotive Recall & Warranty Management: 2018 - 2030 ($ Million)
  • Figure 34: Global Big Data Revenue in Automotive Parts Inventory & Pricing Optimization: 2018 - 2030 ($ Million)
  • Figure 35: Global Big Data Revenue in Automotive Dealer Management & Customer Support Services: 2018 - 2030 ($ Million)
  • Figure 36: Global Big Data Revenue in UBI (Usage-Based Insurance): 2018 - 2030 ($ Million)
  • Figure 37: Global Big Data Revenue in Autonomous & Semi-Autonomous Driving: 2018 - 2030 ($ Million)
  • Figure 38: Global Big Data Revenue in Intelligent Transportation: 2018 - 2030 ($ Million)
  • Figure 39: Global Big Data Revenue in Fleet Management: 2018 - 2030 ($ Million)
  • Figure 40: Global Big Data Revenue in Driver Safety & Vehicle Cyber Security: 2018 - 2030 ($ Million)
  • Figure 41: Global Big Data Revenue in In-Vehicle Experience, Navigation & Infotainment: 2018 - 2030 ($ Million)
  • Figure 42: Global Big Data Revenue in Ride Sourcing, Sharing & Rentals: 2018 - 2030 ($ Million)
  • Figure 43: Global Big Data Revenue in Automotive Marketing & Sales: 2018 - 2030 ($ Million)
  • Figure 44: Global Big Data Revenue in Automotive Customer Retention: 2018 - 2030 ($ Million)
  • Figure 45: Global Big Data Revenue in Automotive Third Party Monetization: 2018 - 2030 ($ Million)
  • Figure 46: Global Big Data Revenue in Other Automotive Industry Use Cases: 2018 - 2030 ($ Million)
  • Figure 47: Big Data Revenue in the Automotive Industry, by Region: 2018 - 2030 ($ Million)
  • Figure 48: Asia Pacific Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 49: Asia Pacific Big Data Revenue in the Automotive Industry, by Country: 2018 - 2030 ($ Million)
  • Figure 50: Australia Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 51: China Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 52: India Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 53: Indonesia Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 54: Japan Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 55: Malaysia Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 56: Pakistan Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 57: Philippines Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 58: Singapore Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 59: South Korea Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 60: Taiwan Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 61: Thailand Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 62: Rest of Asia Pacific Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 63: Eastern Europe Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 64: Eastern Europe Big Data Revenue in the Automotive Industry, by Country: 2018 - 2030 ($ Million)
  • Figure 65: Czech Republic Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 66: Poland Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 67: Russia Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 68: Rest of Eastern Europe Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 69: Latin & Central America Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 70: Latin & Central America Big Data Revenue in the Automotive Industry, by Country: 2018 - 2030 ($ Million)
  • Figure 71: Argentina Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 72: Brazil Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 73: Mexico Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 74: Rest of Latin & Central America Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 75: Middle East & Africa Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 76: Middle East & Africa Big Data Revenue in the Automotive Industry, by Country: 2018 - 2030 ($ Million)
  • Figure 77: Israel Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 78: Qatar Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 79: Saudi Arabia Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 80: South Africa Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 81: UAE Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 82: Rest of the Middle East & Africa Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 83: North America Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 84: North America Big Data Revenue in the Automotive Industry, by Country: 2018 - 2030 ($ Million)
  • Figure 85: Canada Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 86: USA Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 87: Western Europe Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 88: Western Europe Big Data Revenue in the Automotive Industry, by Country: 2018 - 2030 ($ Million)
  • Figure 89: Denmark Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 90: Finland Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 91: France Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 92: Germany Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 93: Italy Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 94: Netherlands Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 95: Norway Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 96: Spain Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 97: Sweden Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 98: UK Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
  • Figure 99: Rest of Western Europe Big Data Revenue in the Automotive Industry: 2018 - 2030 ($ Million)
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