Big Data in the Automotive Industry: 2017 - 2030 - Opportunities, Challenges, Strategies & Forecasts

  • ID: 4229025
  • Report
  • Region: Global
  • 445 Pages
  • SNS Telecom & IT
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Big Data a $2.8 Billion Opportunity in the Automotive Industry

FEATURED COMPANIES

  • 1010data
  • Capgemini
  • Dundas Data Visualization
  • Jedox
  • Nokia
  • SCIO Health Analytics
  • MORE

“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.

The author estimates that Big Data investments in the automotive industry will account for over $2.8 Billion in 2017 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 12% over the next three years.

The “Big Data in the Automotive Industry: 2017 - 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 2017 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.

Key Questions Answered

The report provides answers to the following key questions:

  • How big is the Big Data opportunity in the automotive industry?
  • How is the market 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 automotive OEMs and other stakeholders investing in Big Data?
  • What opportunities exist for Big Data analytics in the automotive industry?
  • Which countries, application areas and use cases will see the highest percentage of Big Data investments in the automotive industry?

Key Findings

The report has the following key findings:

  • In 2017, Big Data vendors will pocket over $2.8 Billion from hardware, software and professional services revenues in the automotive industry. These investments are further expected to grow at a CAGR of approximately 12% over the next three years, eventually accounting for over $4 Billion by the end of 2020.
  • In a bid to improve customer retention, automotive OEMs are heavily relying on Big Data and analytics to integrate an array of data-driven aftermarket services such as predictive vehicle maintenance, real-time mapping and personalized concierge services.
  • In recent years, several prominent partnerships and M&A deals have taken place that highlight the growing importance of Big Data in the automotive industry. For example, tier-1 supplier Delphi recently led an investment round to raise over $25 Million for Otonomo, a startup that has developed a data exchange and marketplace platform for vehicle-generated data.
  • Addressing privacy concerns is necessary in order to monetize the swaths of Big Data that will be generated by a growing installed base of connected vehicles and other segments of the automotive industry.
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FEATURED COMPANIES

  • 1010data
  • Capgemini
  • Dundas Data Visualization
  • Jedox
  • Nokia
  • SCIO Health Analytics
  • MORE

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: 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: 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: 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

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

6: Future Roadmap & Value Chain
6.1 Future Roadmap
6.1.1 2017 - 2020: Growing Investments in Real-Time & Predictive Analytics
6.1.2 2020 - 2025: Large-Scale Monetization of Automotive Big Data
6.1.3 2025 - 2030: Enabling Autonomous Driving & Future IoT Applications
6.2 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

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 Telecommunications 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

8: Market Analysis & 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

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

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 Achieving Customer Retention with Data-Driven Services
10.5 Addressing Privacy Concerns
10.6 The Role of Legislation
10.7 Encouraging Data Sharing in the Automotive Industry
10.8 Assessing the Impact of Self-Driving Vehicles
10.9 Recommendations
10.9.1 Big Data Hardware, Software & Professional Services Providers
10.9.2 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: 2016 (%)
Figure 4: Sensors in an Autonomous Vehicle
Figure 5: Toyota Smart Center Architecture
Figure 6: Big Data Roadmap in the Automotive Industry
Figure 7: Big Data Value Chain in the Automotive Industry
Figure 8: Key Aspects of Big Data Standardization
Figure 9: Global Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 10: Global Big Data Revenue in the Automotive Industry, by Hardware, Software & Professional Services: 2017 - 2030 ($ Million)
Figure 11: Global Big Data Revenue in the Automotive Industry, by Submarket: 2017 - 2030 ($ Million)
Figure 12: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 13: Global Big Data Networking Infrastructure Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 14: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 15: Global Big Data SQL Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 16: Global Big Data NoSQL Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 17: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 18: Global Big Data Cloud Platforms Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 19: Global Big Data Professional Services Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 20: Global Big Data Revenue in the Automotive Industry, by Application Area: 2017 - 2030 ($ Million)
Figure 21: Global Big Data Revenue in Automotive Product Development, Manufacturing & Supply Chain: 2017 - 2030 ($ Million)
Figure 22: Global Big Data Revenue in Automotive After-Sales, Warranty & Dealer Management: 2017 - 2030 ($ Million)
Figure 23: Global Big Data Revenue in Connected Vehicles & Intelligent Transportation: 2017 - 2030 ($ Million)
Figure 24: Global Big Data Revenue in Automotive Marketing, Sales & Other Applications: 2017 - 2030 ($ Million)
Figure 25: Global Big Data Revenue in the Automotive Industry, by Use Case: 2017 - 2030 ($ Million)
Figure 26: Global Big Data Revenue in Automotive Supply Chain Management: 2017 - 2030 ($ Million)
Figure 27: Global Big Data Revenue in Automotive Manufacturing: 2017 - 2030 ($ Million)
Figure 28: Global Big Data Revenue in Automotive Product Design & Planning: 2017 - 2030 ($ Million)
Figure 29: Global Big Data Revenue in Automotive Predictive Maintenance & Real-Time Diagnostics: 2017 - 2030 ($ Million)
Figure 30: Global Big Data Revenue in Automotive Recall & Warranty Management: 2017 - 2030 ($ Million)
Figure 31: Global Big Data Revenue in Automotive Parts Inventory & Pricing Optimization: 2017 - 2030 ($ Million)
Figure 32: Global Big Data Revenue in Automotive Dealer Management & Customer Support Services: 2017 - 2030 ($ Million)
Figure 33: Global Big Data Revenue in UBI (Usage-Based Insurance): 2017 - 2030 ($ Million)
Figure 34: Global Big Data Revenue in Autonomous & Semi-Autonomous Driving: 2017 - 2030 ($ Million)
Figure 35: Global Big Data Revenue in Intelligent Transportation: 2017 - 2030 ($ Million)
Figure 36: Global Big Data Revenue in Fleet Management: 2017 - 2030 ($ Million)
Figure 37: Global Big Data Revenue in Driver Safety & Vehicle Cyber Security: 2017 - 2030 ($ Million)
Figure 38: Global Big Data Revenue in In-Vehicle Experience, Navigation & Infotainment: 2017 - 2030 ($ Million)
Figure 39: Global Big Data Revenue in Ride Sourcing, Sharing & Rentals: 2017 - 2030 ($ Million)
Figure 40: Global Big Data Revenue in Automotive Marketing & Sales: 2017 - 2030 ($ Million)
Figure 41: Global Big Data Revenue in Automotive Customer Retention: 2017 - 2030 ($ Million)
Figure 42: Global Big Data Revenue in Automotive Third Party Monetization: 2017 - 2030 ($ Million)
Figure 43: Global Big Data Revenue in Other Automotive Industry Use Cases: 2017 - 2030 ($ Million)
Figure 44: Big Data Revenue in the Automotive Industry, by Region: 2017 - 2030 ($ Million)
Figure 45: Asia Pacific Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 46: Asia Pacific Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million)
Figure 47: Australia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 48: China Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 49: India Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 50: Indonesia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 51: Japan Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 52: Malaysia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 53: Pakistan Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 54: Philippines Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 55: Singapore Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 56: South Korea Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 57: Taiwan Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 58: Thailand Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 59: Rest of Asia Pacific Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 60: Eastern Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 61: Eastern Europe Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million)
Figure 62: Czech Republic Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 63: Poland Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 64: Russia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 65: Rest of Eastern Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 66: Latin & Central America Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 67: Latin & Central America Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million)
Figure 68: Argentina Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 69: Brazil Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 70: Mexico Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 71: Rest of Latin & Central America Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 72: Middle East & Africa Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 73: Middle East & Africa Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million)
Figure 74: Israel Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 75: Qatar Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 76: Saudi Arabia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 77: South Africa Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 78: UAE Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 79: Rest of the Middle East & Africa Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 80: North America Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 81: North America Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million)
Figure 82: Canada Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 83: USA Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 84: Western Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 85: Western Europe Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million)
Figure 86: Denmark Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 87: Finland Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 88: France Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 89: Germany Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 90: Italy Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 91: Netherlands Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 92: Norway Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 93: Spain Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 94: Sweden Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 95: UK Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)
Figure 96: Rest of Western Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million)

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FEATURED COMPANIES

  • 1010data
  • Capgemini
  • Dundas Data Visualization
  • Jedox
  • Nokia
  • SCIO Health Analytics
  • MORE

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
  • Business case, key applications and use cases in the automotive industry
  • 30 case studies of Big Data investments by automotive OEMs and other stakeholders
  • Future roadmap and value chain
  • Company profiles and strategies of over 240 Big Data vendors
  • Strategic recommendations for Big Data vendors, automotive OEMs and other stakeholders
  • 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

Application Areas

  • Product Development, Manufacturing & Supply Chain
  • After-Sales, Warranty & Dealer Management
  • Connected Vehicles & Intelligent Transportation
  • Marketing, Sales & Other Applications

Use Cases

  • Supply Chain Management
  • Manufacturing
  • Product Design & Planning
  • Predictive Maintenance & Real-Time Diagnostics
  • Recall & Warranty Management
  • Parts Inventory & Pricing Optimization
  • Dealer Management & Customer Support Services
  • UBI (Usage-Based Insurance)
  • Autonomous & Semi-Autonomous Driving
  • Intelligent Transportation
  • Fleet Management
  • Driver Safety & Vehicle Cyber Security
  • In-Vehicle Experience, Navigation & Infotainment
  • Ride Sourcing, Sharing & Rentals
  • Marketing & Sales
  • Customer Retention
  • Third Party Monetization
  • Other Use Cases

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

Note: Product cover images may vary from those shown
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  • 1010data
  • ACEA (European Automobile Manufacturers’ Association)
  • AFS Technologies
  • AMD (Advanced Micro Devices)
  • ARM
  • ASF (Apache Software Foundation)
  • AWS (Amazon Web Services)
  • Absolutdata
  • Accenture
  • Actian Corporation
  • Adaptive Insights
  • Advizor Solutions
  • AeroSpike
  • Alation
  • Algorithmia
  • Alibaba
  • Alliance of Automobile Manufacturers
  • Alluxio
  • Alphabet
  • Alpine Data
  • Alteryx
  • Apixio
  • Arcadia Data
  • Arimo
  • AtScale
  • Attivio
  • Attunity
  • Audi
  • Automated Insights
  • automotiveMastermind
  • Axiomatics
  • Ayasdi
  • BCG (Boston Consulting Group)
  • BMC Software
  • BMW
  • BOARD International
  • Basho Technologies
  • Bedrock Data
  • BetterWorks
  • Big Cloud Analytics
  • Big Panda
  • BigML
  • Birst
  • Bitam
  • Blue Medora
  • BlueData Software
  • BlueTalon
  • Booz Allen Hamilton
  • Boxever
  • CACI International
  • CSA (Cloud Security Alliance)
  • CSCC (Cloud Standards Customer Council)
  • Cambridge Semantics
  • Capgemini
  • Cazena
  • Centrifuge Systems
  • CenturyLink
  • Chartio
  • Cisco Systems
  • Civis Analytics
  • ClearStory Data
  • Cloudability
  • Cloudera
  • Clustrix
  • CognitiveScale
  • Collibra
  • Concurrent Computer Corporation
  • Confluent
  • Contexti
  • Continental
  • Continuum Analytics
  • Couchbase
  • CrowdFlower
  • DDN (DataDirect Networks)
  • DMG  (Data Mining Group)
  • DXC Technology
  • Daimler
  • Dash Labs
  • DataGravity
  • DataRobot
  • DataScience
  • DataStax
  • DataTorrent
  • Databricks
  • Dataiku
  • Datameer
  • Datawatch Corporation
  • Datos IO
  • Decisyon
  • Dell EMC
  • Dell Technologies
  • Deloitte
  • Delphi Automotive
  • Demandbase
  • Denodo Technologies
  • Denso Corporation
  • Digital Reasoning Systems
  • Dimensional Insight
  • Dolphin Enterprise Solutions Corporation
  • Domino Data Lab
  • Domo
  • DriveScale
  • Dundas Data Visualization
  • EXASOL
  • Eligotech
  • Engie
  • Engineering Group (Engineering Ingegneria Informatica)
  • EnterpriseDB
  • eQ Technologic
  • Ericsson
  • FCA (Fiat Chrysler Automobiles)
  • FICO (Fair Isaac Corporation)
  • FTC (U.S. Federal Trade Commission)
  • Facebook
  • Ford Motor Company
  • Fractal Analytics
  • Fujitsu
  • Fuzzy Logix
  • GE (General Electric)
  • GM (General Motors Company)
  • Gainsight
  • Geely (Zhejiang Geely Holding Group)
  • Glassbeam
  • GoodData Corporation
  • Google
  • Greenwave Systems
  • GridGain Systems
  • Groupe PSA
  • Groupe Renault
  • Guavus
  • H2O.ai
  • HDS (Hitachi Data Systems)
  • HERE
  • HPE (Hewlett Packard Enterprise)
  • Hedvig
  • Honda Motor Company
  • Hortonworks
  • Huawei
  • Hyundai Motor Company
  • IBM Corporation
  • iDashboards
  • IEC (International Electrotechnical Commission)
  • IEEE (Institute of Electrical and Electronics Engineers)
  • INCITS (InterNational Committee for Information Technology Standards)
  • ISO (International Organization for Standardization)
  • Impetus Technologies
  • Incorta
  • InetSoft Technology Corporation
  • Infer
  • Infor
  • Informatica Corporation
  • Information Builders
  • Infosys
  • Infoworks
  • InsightSquared
  • Insightsoftware.com
  • Intel Corporation
  • InterSystems Corporation
  • Interana
  • Jaguar Land Rover
  • Jedox
  • Jethro
  • Jinfonet Software
  • Juniper Networks
  • KALEAO
  • KDDI Corporation
  • KNIME
  • Keen IO
  • Kia Motor Corporation
  • Kinetica
  • Kognitio
  • Kyvos Insights
  • Lavastorm
  • Lexalytics
  • Lexmark International
  • Lexus
  • Linux Foundation
  • Logi Analytics
  • Longview Solutions
  • Looker Data Sciences
  • LucidWorks
  • Luminoso Technologies
  • Lytx
  • METI (Ministry of Economy, Trade and Industry, Japan)
  • Maana
  • Magento Commerce
  • Manthan Software Services
  • MapD Technologies
  • MapR Technologies
  • MariaDB Corporation
  • MarkLogic Corporation
  • Mathworks
  • Mazda Motor Corporation
  • MemSQL
  • Mercedes-Benz
  • Metric Insights
  • Michelin
  • MicroStrategy
  • Microsoft Corporation
  • Minitab
  • MongoDB
  • Mu Sigma
  • NEC Corporation
  • NIST (U.S. National Institute of Standards and Technology)
  • NTT Data Corporation
  • NTT Group
  • NVIDIA Corporation
  • NYC DOT (New York City Department of Transportation)
  • Neo Technology
  • NetApp
  • Nimbix
  • Nissan Motor Company
  • Nokia
  • Numerify
  • NuoDB
  • Nutonian
  • OASIS (Organization for the Advancement of Structured Information Standards)
  • ODCA (Open Data Center Alliance)
  • ODPi (Open Ecosystem of Big Data)
  • ODaF (Open Data Foundation)
  • OGC (Open Geospatial Consortium)
  • Oblong Industries
  • OpenText Corporation
  • Opera Solutions
  • Optimal Plus
  • Oracle Corporation
  • Otonomo
  • Palantir Technologies
  • Panorama Software
  • Paxata
  • Pentaho Corporation
  • Pepperdata
  • Phocas Software
  • Pivotal Software
  • Prognoz
  • Progress Software Corporation
  • PwC (PricewaterhouseCoopers International)
  • Pyramid Analytics
  • Qlik
  • Quantum Corporation
  • Qubole
  • RStudio
  • Rackspace
  • Radius Intelligence
  • RapidMiner
  • Recorded Future
  • Red Hat
  • RedPoint Global
  • Redis Labs
  • Reltio
  • Robert Bosch
  • Rocket Fuel
  • Rosenberger
  • Ryft Systems
  • SAIC Motor Corporation
  • SAP
  • SAS Institute
  • SCIO Health Analytics
  • Sailthru
  • Salesforce.com
  • Salient Management Company
  • Samsung Group
  • ScaleDB
  • ScaleOut Software
  • Seagate Technology
  • SiSense
  • Sinequa
  • SnapLogic
  • Snowflake Computing
  • Software AG
  • Splice Machine
  • Splunk
  • Sqrrl
  • Strategy Companion Corporation
  • StreamSets
  • Striim
  • Subaru
  • Sumo Logic
  • Supermicro (Super Micro Computer)
  • Suzuki Motor Corporation
  • Syncsort
  • SynerScope
  • TARGIT
  • TCS (Tata Consultancy Services)
  • THTA (Tokyo Hire-Taxi Association)
  • TIBCO Software
  • TM Forum
  • TPC (Transaction Processing Performance Council)
  • Tableau Software
  • Talena
  • Talend
  • Tamr
  • Teradata Corporation
  • Tesla
  • The Floow
  • ThoughtSpot
  • Tidemark
  • Toshiba Corporation
  • Toyota Motor Corporation
  • Trifacta
  • Uber Technologies
  • Unravel Data
  • VMware
  • Valens
  • Volkswagen Group
  • VoltDB
  • Volvo Cars
  • W3C (World Wide Web Consortium)
  • Waterline Data
  • Western Digital Corporation
  • WiPro
  • Workday
  • Xevo
  • Xplenty
  • Yellowfin International
  • Yseop
  • Zendesk
  • Zoomdata
  • Zucchetti
Note: Product cover images may vary from those shown
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