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

Published By :

SNS Telecom & IT

Published Date : Jul 2018

Category :

ICT

No. of Pages : 501 Pages

“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.

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, application areas and use cases in the automotive industry
  •  Over 35 case studies of Big Data investments by automotive OEMs and other stakeholders
  •  Future roadmap and value chain
  •  Profiles and strategies of over 270 leading and emerging Big Data ecosystem players
  •  Strategic recommendations for Big Data vendors, automotive OEMs and other stakeholders
  •  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

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

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 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 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 2018, Big Data vendors will pocket more than $3.3 Billion from hardware, software and professional services revenues in the automotive industry. These investments are further expected to grow at a CAGR of approximately 16% over the next three years, eventually accounting for over $5 Billion by the end of 2021.
  •  Through the use of Big Data technologies, automotive OEMs and other stakeholders are beginning to exploit vehicle-generated data assets in a number of innovative ways ranging from predictive vehicle maintenance and UBI (Usage-Based Insurance) to real-time mapping, personalized concierge, autonomous driving and beyond.
  •  Edge analytics, which refers to the processing and analysis of information closer to the point of origin, is increasingly becoming an indispensable capability for applications such as autonomous driving where real-time data – from cameras, LiDAR and other on-board sensors – needs to be acted upon instantly and reliably.
  •  Privacy continues to remain a major concern, and ensuring the protection of sensitive information – through creative anonymization and dedicated cybersecurity investments  – 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.

List of Companies Mentioned

  • 1010data
  • Absolutdata
  • Accenture
  • ACEA (European Automobile Manufacturers’ Association)
  • Actian Corporation
  • Adaptive Insights
  • Adobe Systems
  • Advizor Solutions
  • AeroSpike
  • AFS Technologies
  • Alation
  • Algorithmia
  • Allstate Corporation
  • Alluxio
  • Alphabet
  • ALTEN
  • Alteryx
  • AMD (Advanced Micro Devices)
  • Anaconda
  • Apixio
  • Arcadia Data
  • Arimo
  • Arity
  • ARM
  • ASF (Apache Software Foundation)
  • AtScale
  • Attivio
  • Attunity
  • Audi
  • Automated Insights
  • Automobili Lamborghini
  • automotiveMastermind
  • AVORA
  • AWS (Amazon Web Services)
  • Axiomatics
  • Ayasdi
  • BackOffice Associates
  • Basho Technologies
  • BCG (Boston Consulting Group)
  • Bedrock Data
  • BetterWorks
  • Big Panda
  • BigML
  • Birst
  • Bitam
  • Blue Medora
  • BlueData Software
  • BlueTalon
  • BMC Software
  • BMW
  • BOARD International
  • Booz Allen Hamilton
  • Bosch
  • Boxever
  • CACI International
  • Cambridge Semantics
  • Capgemini
  • Cazena
  • Centrifuge Systems
  • CenturyLink
  • Chartio
  • Cisco Systems
  • Citroën
  • Civis Analytics
  • ClearStory Data
  • Cloudability
  • Cloudera
  • Cloudian
  • Clustrix
  • CognitiveScale
  • Collibra
  • Concurrent Technology
  • Confluent
  • Contexti
  • Continental
  • Couchbase
  • Cox Automotive
  • Cox Enterprises
  • Crate.io
  • Cray
  • CSA (Cloud Security Alliance)
  • CSCC (Cloud Standards Customer Council)
  • Daimler
  • Dash Labs
  • Databricks
  • Dataiku
  • Datalytyx
  • Datameer
  • DataRobot
  • DataStax
  • Datawatch Corporation
  • Datos IO
  • DDN (DataDirect Networks)
  • Decisyon
  • Dell Technologies
  • Deloitte
  • Delphi Automotive
  • Demandbase
  • Denodo Technologies
  • Denso Corporation
  • Dianomic Systems
  • Digital Reasoning Systems
  • Dimensional Insight
  • DMG  (Data Mining Group)
  • Dolphin Enterprise Solutions Corporation
  • Domino Data Lab
  • Domo
  • Dongfeng Motor Corporation
  • Dremio
  • DriveScale
  • Druva
  • DS Automobiles
  • Ducati
  • Dundas Data Visualization
  • DXC Technology
  • Elastic
  • Engineering Group (Engineering Ingegneria Informatica)
  • EnterpriseDB Corporation
  • eQ Technologic
  • Ericsson
  • Erwin
  • EV? (Big Cloud Analytics)
  • EXASOL
  • EXL (ExlService Holdings)
  • Facebook
  • FCA (Fiat Chrysler Automobiles)
  • FICO (Fair Isaac Corporation)
  • Figure Eight
  • FogHorn Systems
  • Ford Motor Company
  • Fractal Analytics
  • Franz
  • Fujitsu
  • Fuzzy Logix
  • Gainsight
  • GE (General Electric)
  • Geely (Zhejiang Geely Holding Group)
  • Glassbeam
  • GM (General Motors Company)
  • GoodData Corporation
  • Google
  • Grakn Labs
  • Greenwave Systems
  • GridGain Systems
  • Groupe PSA
  • Groupe Renault
  • Guavus
  • H2O.ai
  • Hanse Orga Group
  • HarperDB
  • HCL Technologies
  • Hedvig
  • HERE
  • Hitachi Vantara
  • Honda Motor Company
  • Hortonworks
  • HPE (Hewlett Packard Enterprise)
  • Huawei
  • HVR
  • HyperScience
  • HyTrust
  • Hyundai Motor Company
  • IBM Corporation
  • iDashboards
  • IDERA
  • IEC (International Electrotechnical Commission)
  • IEEE (Institute of Electrical and Electronics Engineers)
  • Ignite Technologies
  • Imanis Data
  • Impetus Technologies
  • INCITS (InterNational Committee for Information Technology Standards)
  • Incorta
  • InetSoft Technology Corporation
  • InfluxData
  • Infogix
  • Infor
  • Informatica
  • Information Builders
  • Infosys
  • Infoworks
  • Insightsoftware.com
  • InsightSquared
  • Intel Corporation
  • Interana
  • InterSystems Corporation
  • ISO (International Organization for Standardization)
  • ITU (International Telecommunication Union)
  • Jaguar Land Rover
  • Jedox
  • Jethro
  • Jinfonet Software
  • Juniper Networks
  • KALEAO
  • KDDI Corporation
  • Keen IO
  • Keyrus
  • Kinetica
  • KNIME
  • Kognitio
  • Kyvos Insights
  • LeanXcale
  • Lexalytics
  • Lexmark International
  • Lightbend
  • Linux Foundation
  • Logi Analytics
  • Logical Clocks
  • Longview Solutions
  • Looker Data Sciences
  • LucidWorks
  • Luminoso Technologies
  • Lytx
  • Maana
  • Manthan Software Services
  • MapD Technologies
  • MapR Technologies
  • MariaDB Corporation
  • MarkLogic Corporation
  • Mathworks
  • Mazda Motor Corporation
  • Melissa
  • MemSQL
  • Mercedes-Benz
  • METI (Ministry of Economy, Trade and Industry, Japan)
  • Metric Insights
  • Michelin
  • Microsoft Corporation
  • MicroStrategy
  • Minitab
  • Mobileye
  • MongoDB
  • Mu Sigma
  • NEC Corporation
  • Neo4j
  • NetApp
  • Nimbix
  • Nissan Motor Company
  • Nokia
  • NTT Data Corporation
  • NTT DoCoMo
  • Numerify
  • NuoDB
  • NVIDIA Corporation
  • OASIS (Organization for the Advancement of Structured Information Standards)
  • Objectivity
  • Oblong Industries
  • ODaF (Open Data Foundation)
  • ODCA (Open Data Center Alliance)
  • OGC (Open Geospatial Consortium)
  • OpenText Corporation
  • Opera Solutions
  • Optimal Plus
  • Oracle Corporation
  • Otonomo
  • Palantir Technologies
  • Panasonic Corporation
  • Panorama Software
  • Paxata
  • Pepperdata
  • Peugeot
  • Phocas Software
  • Pivotal Software
  • Prognoz
  • Progress Software Corporation
  • Progressive Corporation
  • Provalis Research
  • Pure Storage
  • PwC (PricewaterhouseCoopers International)
  • Pyramid Analytics
  • Qlik
  • Qrama/Tengu
  • Quantum Corporation
  • Qubole
  • Rackspace
  • Radius Intelligence
  • RapidMiner
  • Recorded Future
  • Red Hat
  • Redis Labs
  • RedPoint Global
  • Reltio
  • RStudio
  • Rubrik
  • Ryft
  • SAIC Motor Corporation
  • Sailthru
  • Salesforce.com
  • Salient Management Company
  • Samsung Group
  • SAP
  • SAS Institute
  • ScaleOut Software
  • Seagate Technology
  • Sinequa
  • SiSense
  • Sizmek
  • SnapLogic
  • Snowflake Computing
  • Software AG
  • Splice Machine
  • Splunk
  • Strategy Companion Corporation
  • Stratio
  • Streamlio
  • StreamSets
  • Striim
  • Subaru
  • Sumo Logic
  • Supermicro (Super Micro Computer)
  • Suzuki Motor Corporation
  • Syncsort
  • SynerScope
  • SYNTASA
  • Tableau Software
  • Talend
  • Tamr
  • TARGIT
  • Tata Motors
  • TCS (Tata Consultancy Services)
  • Teradata Corporation
  • Tesla
  • Thales
  • ThoughtSpot
  • THTA (Tokyo Hire-Taxi Association)
  • TIBCO Software
  • Tidemark
  • TM Forum
  • Toshiba Corporation
  • Toyota Motor Corporation
  • TPC (Transaction Processing Performance Council)
  • Transwarp
  • Trifacta
  • U.S. FTC (Federal Trade Commission)
  • U.S. NIST (National Institute of Standards and Technology)
  • U.S. Xpress
  • Uber Technologies
  • Unifi Software
  • Unravel Data
  • Valens
  • VANTIQ
  • Vecima Networks
  • VMware
  • Volkswagen Group
  • VoltDB
  • Volvo Cars
  • W3C (World Wide Web Consortium)
  • WANdisco
  • Waterline Data
  • Western Digital Corporation
  • WhereScape
  • WiPro
  • Wolfram Research
  • Workday
  • Xevo
  • Xplenty
  • Yellowfin BI
  • Yseop
  • Zendesk
  • Zoomdata
  • Zucchetti

Table of Contents

1 Chapter 1: Introduction 24
1.1 Executive Summary 24
1.2 Topics Covered 26
1.3 Forecast Segmentation 27
1.4 Key Questions Answered 30
1.5 Key Findings 31
1.6 Methodology 32
1.7 Target Audience 33
1.8 Companies & Organizations Mentioned 34

2 Chapter 2: An Overview of Big Data 38
2.1 What is Big Data? 38
2.2 Key Approaches to Big Data Processing 38
2.2.1 Hadoop 39
2.2.2 NoSQL 41
2.2.3 MPAD (Massively Parallel Analytic Databases) 41
2.2.4 In-Memory Processing 42
2.2.5 Stream Processing Technologies 42
2.2.6 Spark 43
2.2.7 Other Databases & Analytic Technologies 43
2.3 Key Characteristics of Big Data 44
2.3.1 Volume 44
2.3.2 Velocity 44
2.3.3 Variety 44
2.3.4 Value 45
2.4 Market Growth Drivers 45
2.4.1 Awareness of Benefits 45
2.4.2 Maturation of Big Data Platforms 45
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 46
2.4.4 Growth of Data Volume, Velocity & Variety 46
2.4.5 Vendor Commitments & Partnerships 46
2.4.6 Technology Trends Lowering Entry Barriers 47
2.5 Market Barriers 47
2.5.1 Lack of Analytic Specialists 47
2.5.2 Uncertain Big Data Strategies 47
2.5.3 Organizational Resistance to Big Data Adoption 48
2.5.4 Technical Challenges: Scalability & Maintenance 48
2.5.5 Security & Privacy Concerns 48

3 Chapter 3: Big Data Analytics 49
3.1 What are Big Data Analytics? 49
3.2 The Importance of Analytics 49
3.3 Reactive vs. Proactive Analytics 50
3.4 Customer vs. Operational Analytics 50
3.5 Technology & Implementation Approaches 51
3.5.1 Grid Computing 51
3.5.2 In-Database Processing 51
3.5.3 In-Memory Analytics 52
3.5.4 Machine Learning & Data Mining 52
3.5.5 Predictive Analytics 53
3.5.6 NLP (Natural Language Processing) 53
3.5.7 Text Analytics 54
3.5.8 Visual Analytics 54
3.5.9 Graph Analytics 55
3.5.10 Social Media, IT & Telco Network Analytics 55

4 Chapter 4: Business Case & Applications in the Automotive Industry 57
4.1 Overview & Investment Potential 57
4.2 Industry Specific Market Growth Drivers 58
4.3 Industry Specific Market Barriers 59
4.4 Key Applications 60
4.4.1 Product Development, Manufacturing & Supply Chain 60
4.4.1.1 Optimizing the Supply Chain 60
4.4.1.2 Eliminating Manufacturing Defects 60
4.4.1.3 Customer-Driven Product Design & Planning 61
4.4.2 After-Sales, Warranty & Dealer Management 61
4.4.2.1 Predictive Maintenance & Real-Time Diagnostics 61
4.4.2.2 Streamlining Recalls & Warranty 62
4.4.2.3 Parts Inventory & Pricing Optimization 62
4.4.2.4 Dealer Management & Customer Support Services 63
4.4.3 Connected Vehicles & Intelligent Transportation 63
4.4.3.1 UBI (Usage-Based Insurance) 63
4.4.3.2 Autonomous & Semi-Autonomous Driving 64
4.4.3.3 Intelligent Transportation 66
4.4.3.4 Fleet Management 66
4.4.3.5 Driver Safety & Vehicle Cyber Security 67
4.4.3.6 In-Vehicle Experience, Navigation & Infotainment 67
4.4.3.7 Ride Sourcing, Sharing & Rentals 67
4.4.4 Marketing, Sales & Other Applications 68
4.4.4.1 Marketing & Sales 68
4.4.4.2 Customer Retention 68
4.4.4.3 Third Party Monetization 69
4.4.4.4 Other Applications 69

5 Chapter 5: Automotive Industry Case Studies 71
5.1 Automotive OEMs 71
5.1.1 Audi: Facilitating Efficient Production Processes with Big Data 71
5.1.2 BMW: Eliminating Defects in New Vehicle Models with Big Data 73
5.1.3 Daimler: Ensuring Quality Assurance with Big Data 74
5.1.4 Dongfeng Motor Corporation: Enriching Network-Connected Autonomous Vehicles with Big Data 75
5.1.5 FCA (Fiat Chrysler Automobiles): Enhancing Dealer Management with Big Data 76
5.1.6 Ford Motor Company: Making Efficient Transportation Decisions with Big Data 77
5.1.7 GM (General Motors Company): Personalizing In-Vehicle Experience with Big Data 79
5.1.8 Groupe PSA: Reducing Industrial Energy Bills with Big Data 80
5.1.9 Groupe Renault: Boosting Driver Safety with Big Data 82
5.1.10 Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data 83
5.1.11 Hyundai Motor Company: Empowering Connected & Self-Driving Cars with Big Data 85
5.1.12 Jaguar Land Rover: Realizing Better & Cheaper Vehicle Designs with Big Data 86
5.1.13 Mazda Motor Corporation: Creating Better Engines with Big Data 87
5.1.14 Nissan Motor Company: Leveraging Big Data to Drive After-Sales Business Growth 88
5.1.15 SAIC Motor Corporation: Transforming Stressful Driving to Enjoyable Moments with Big Data 90
5.1.16 Subaru: Turbocharging Dealer Interaction with Big Data 91
5.1.17 Suzuki Motor Corporation: Accelerating Vehicle Design and Innovation with Big Data 92
5.1.18 Tesla: Achieving Customer Loyalty with Big Data 93
5.1.19 Toyota Motor Corporation: Powering Smart Cars with Big Data 94
5.1.20 Volkswagen Group: Transitioning to End-to-End Mobility Solutions with Big Data 96
5.1.21 Volvo Cars: Reducing Breakdowns and Failures with Big Data 98
5.2 Other Stakeholders 99
5.2.1 Allstate Corporation & Arity: Making Transportation Safer & Smarter with Big Data 99
5.2.2 automotiveMastermind: Helping Automotive Dealerships Increase Sales with Big Data 101
5.2.3 Continental: Making Vehicles Safer with Big Data 102
5.2.4 Cox Automotive: Transforming the Used Vehicle Lifecycle with Big Data 103
5.2.5 Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data 104
5.2.6 Delphi Automotive: Monetizing Connected Vehicles with Big Data 105
5.2.7 Denso Corporation: Enabling Hazard Prediction with Big Data 106
5.2.8 HERE: Easing Traffic Congestion with Big Data 107
5.2.9 Lytx: Ensuring Road Safety with Big Data 108
5.2.10 Michelin: Optimizing Tire Manufacturing with Big Data 109
5.2.11 Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data 110
5.2.12 Bosch: Empowering Fleet Management & Vehicle Insurance with Big Data 113
5.2.13 THTA (Tokyo Hire-Taxi Association): Making Connected Taxis a Reality with Big Data 114
5.2.14 Uber Technologies: Revolutionizing Ride Sourcing with Big Data 115
5.2.15 U.S. Xpress: Driving Fuel-Savings with Big Data 116

6 Chapter 6: Future Roadmap & Value Chain 118
6.1 Future Roadmap 118
6.1.1 Pre-2020: Investments in Advanced Analytics for Vehicle-Related Services 118
6.1.2 2020 – 2025: Proliferation of Real-Time Edge Analytics & Automotive Data Monetization 119
6.1.3 2025 – 2030: Towards Fully Autonomous Driving & Future IoT Applications 120
6.2 The Big Data Value Chain 121
6.2.1 Hardware Providers 121
6.2.1.1 Storage & Compute Infrastructure Providers 121
6.2.1.2 Networking Infrastructure Providers 122
6.2.2 Software Providers 122
6.2.2.1 Hadoop & Infrastructure Software Providers 123
6.2.2.2 SQL & NoSQL Providers 123
6.2.2.3 Analytic Platform & Application Software Providers 123
6.2.2.4 Cloud Platform Providers 123
6.2.3 Professional Services Providers 124
6.2.4 End-to-End Solution Providers 124
6.2.5 Automotive Industry 124

7 Chapter 7: Standardization & Regulatory Initiatives 125
7.1 ASF (Apache Software Foundation) 125
7.1.1 Management of Hadoop 125
7.1.2 Big Data Projects Beyond Hadoop 125
7.2 CSA (Cloud Security Alliance) 129
7.2.1 BDWG (Big Data Working Group) 129
7.3 CSCC (Cloud Standards Customer Council) 129
7.3.1 Big Data Working Group 130
7.4 DMG (Data Mining Group) 130
7.4.1 PMML (Predictive Model Markup Language) Working Group 131
7.4.2 PFA (Portable Format for Analytics) Working Group 131
7.5 IEEE (Institute of Electrical and Electronics Engineers) 131
7.5.1 Big Data Initiative 132
7.6 INCITS (InterNational Committee for Information Technology Standards) 133
7.6.1 Big Data Technical Committee 133
7.7 ISO (International Organization for Standardization) 134
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 134
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 135
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 135
7.7.4 ISO/IEC JTC 1/WG 9: Big Data 135
7.7.5 Collaborations with Other ISO Work Groups 136
7.8 ITU (International Telecommunication Union) 137
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities 137
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 138
7.8.3 Other Relevant Work 138
7.9 Linux Foundation 139
7.9.1 ODPi (Open Ecosystem of Big Data) 139
7.10 NIST (National Institute of Standards and Technology) 139
7.10.1 NBD-PWG (NIST Big Data Public Working Group) 139
7.11 OASIS (Organization for the Advancement of Structured Information Standards) 140
7.11.1 Technical Committees 140
7.12 ODaF (Open Data Foundation) 141
7.12.1 Big Data Accessibility 141
7.13 ODCA (Open Data Center Alliance) 141
7.13.1 Work on Big Data 142
7.14 OGC (Open Geospatial Consortium) 142
7.14.1 Big Data DWG (Domain Working Group) 142
7.15 TM Forum 142
7.15.1 Big Data Analytics Strategic Program 143
7.16 TPC (Transaction Processing Performance Council) 143
7.16.1 TPC-BDWG (TPC Big Data Working Group) 143
7.17 W3C (World Wide Web Consortium) 143
7.17.1 Big Data Community Group 144
7.17.2 Open Government Community Group 144

8 Chapter 8: Market Sizing & Forecasts 145
8.1 Global Outlook for Big Data in the Automotive Industry 145
8.2 Hardware, Software & Professional Services Segmentation 146
8.3 Horizontal Submarket Segmentation 147
8.4 Hardware Submarkets 147
8.4.1 Storage and Compute Infrastructure 147
8.4.2 Networking Infrastructure 148
8.5 Software Submarkets 148
8.5.1 Hadoop & Infrastructure Software 148
8.5.2 SQL 149
8.5.3 NoSQL 149
8.5.4 Analytic Platforms & Applications 150
8.5.5 Cloud Platforms 150
8.6 Professional Services Submarket 151
8.6.1 Professional Services 151
8.7 Application Area Segmentation 152
8.7.1 Product Development, Manufacturing & Supply Chain 152
8.7.2 After-Sales, Warranty & Dealer Management 153
8.7.3 Connected Vehicles & Intelligent Transportation 153
8.7.4 Marketing, Sales & Other Applications 154
8.8 Use Case Segmentation 155
8.9 Product Development, Manufacturing & Supply Chain Use Cases 156
8.9.1 Supply Chain Management 156
8.9.2 Manufacturing 156
8.9.3 Product Design & Planning 157
8.10 After-Sales, Warranty & Dealer Management Use Cases 157
8.10.1 Predictive Maintenance & Real-Time Diagnostics 157
8.10.2 Recall & Warranty Management 158
8.10.3 Parts Inventory & Pricing Optimization 158
8.10.4 Dealer Management & Customer Support Services 159
8.11 Connected Vehicles & Intelligent Transportation Use Cases 159
8.11.1 UBI (Usage-Based Insurance) 159
8.11.2 Autonomous & Semi-Autonomous Driving 160
8.11.3 Intelligent Transportation 160
8.11.4 Fleet Management 161
8.11.5 Driver Safety & Vehicle Cyber Security 161
8.11.6 In-Vehicle Experience, Navigation & Infotainment 162
8.11.7 Ride Sourcing, Sharing & Rentals 162
8.12 Marketing, Sales & Other Application Use Cases 163
8.12.1 Marketing & Sales 163
8.12.2 Customer Retention 163
8.12.3 Third Party Monetization 164
8.12.4 Other Use Cases 164
8.13 Regional Outlook 165
8.14 Asia Pacific 165
8.14.1 Country Level Segmentation 166
8.14.2 Australia 166
8.14.3 China 167
8.14.4 India 167
8.14.5 Indonesia 168
8.14.6 Japan 168
8.14.7 Malaysia 169
8.14.8 Pakistan 169
8.14.9 Philippines 170
8.14.10 Singapore 170
8.14.11 South Korea 171
8.14.12 Taiwan 171
8.14.13 Thailand 172
8.14.14 Rest of Asia Pacific 172
8.15 Eastern Europe 173
8.15.1 Country Level Segmentation 173
8.15.2 Czech Republic 174
8.15.3 Poland 174
8.15.4 Russia 175
8.15.5 Rest of Eastern Europe 175
8.16 Latin & Central America 176
8.16.1 Country Level Segmentation 176
8.16.2 Argentina 177
8.16.3 Brazil 177
8.16.4 Mexico 178
8.16.5 Rest of Latin & Central America 178
8.17 Middle East & Africa 179
8.17.1 Country Level Segmentation 179
8.17.2 Israel 180
8.17.3 Qatar 180
8.17.4 Saudi Arabia 181
8.17.5 South Africa 181
8.17.6 UAE 182
8.17.7 Rest of the Middle East & Africa 182
8.18 North America 183
8.18.1 Country Level Segmentation 183
8.18.2 Canada 184
8.18.3 USA 184
8.19 Western Europe 185
8.19.1 Country Level Segmentation 185
8.19.2 Denmark 186
8.19.3 Finland 186
8.19.4 France 187
8.19.5 Germany 187
8.19.6 Italy 188
8.19.7 Netherlands 188
8.19.8 Norway 189
8.19.9 Spain 189
8.19.10 Sweden 190
8.19.11 UK 190
8.19.12 Rest of Western Europe 191

9 Chapter 9: Vendor Landscape 192
9.1 1010data 192
9.2 Absolutdata 193
9.3 Accenture 194
9.4 Actian Corporation/HCL Technologies 195
9.5 Adaptive Insights 197
9.6 Adobe Systems 198
9.7 Advizor Solutions 200
9.8 AeroSpike 201
9.9 AFS Technologies 202
9.10 Alation 203
9.11 Algorithmia 204
9.12 Alluxio 205
9.13 ALTEN 206
9.14 Alteryx 207
9.15 AMD (Advanced Micro Devices) 208
9.16 Anaconda 209
9.17 Apixio 210
9.18 Arcadia Data 211
9.19 ARM 212
9.20 AtScale 213
9.21 Attivio 214
9.22 Attunity 215
9.23 Automated Insights 216
9.24 AVORA 217
9.25 AWS (Amazon Web Services) 218
9.26 Axiomatics 220
9.27 Ayasdi 221
9.28 BackOffice Associates 222
9.29 Basho Technologies 223
9.30 BCG (Boston Consulting Group) 224
9.31 Bedrock Data 225
9.32 BetterWorks 226
9.33 Big Panda 227
9.34 BigML 228
9.35 Bitam 229
9.36 Blue Medora 230
9.37 BlueData Software 231
9.38 BlueTalon 232
9.39 BMC Software 233
9.40 BOARD International 234
9.41 Booz Allen Hamilton 235
9.42 Boxever 236
9.43 CACI International 237
9.44 Cambridge Semantics 238
9.45 Capgemini 239
9.46 Cazena 240
9.47 Centrifuge Systems 241
9.48 CenturyLink 242
9.49 Chartio 243
9.50 Cisco Systems 244
9.51 Civis Analytics 245
9.52 ClearStory Data 246
9.53 Cloudability 247
9.54 Cloudera 248
9.55 Cloudian 249
9.56 Clustrix 250
9.57 CognitiveScale 251
9.58 Collibra 252
9.59 Concurrent Technology/Vecima Networks 253
9.60 Confluent 254
9.61 Contexti 255
9.62 Couchbase 256
9.63 Crate.io 257
9.64 Cray 258
9.65 Databricks 259
9.66 Dataiku 260
9.67 Datalytyx 261
9.68 Datameer 262
9.69 DataRobot 263
9.70 DataStax 264
9.71 Datawatch Corporation 265
9.72 DDN (DataDirect Networks) 266
9.73 Decisyon 267
9.74 Dell Technologies 268
9.75 Deloitte 269
9.76 Demandbase 270
9.77 Denodo Technologies 271
9.78 Dianomic Systems 272
9.79 Digital Reasoning Systems 273
9.80 Dimensional Insight 274
9.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group 275
9.82 Domino Data Lab 276
9.83 Domo 277
9.84 Dremio 278
9.85 DriveScale 279
9.86 Druva 280
9.87 Dundas Data Visualization 281
9.88 DXC Technology 282
9.89 Elastic 283
9.90 Engineering Group (Engineering Ingegneria Informatica) 284
9.91 EnterpriseDB Corporation 285
9.92 eQ Technologic 286
9.93 Ericsson 287
9.94 Erwin 288
9.95 EV? (Big Cloud Analytics) 289
9.96 EXASOL 290
9.97 EXL (ExlService Holdings) 291
9.98 Facebook 292
9.99 FICO (Fair Isaac Corporation) 293
9.100 Figure Eight 294
9.101 FogHorn Systems 295
9.102 Fractal Analytics 296
9.103 Franz 297
9.104 Fujitsu 298
9.105 Fuzzy Logix 300
9.106 Gainsight 301
9.107 GE (General Electric) 302
9.108 Glassbeam 303
9.109 GoodData Corporation 304
9.110 Google/Alphabet 305
9.111 Grakn Labs 307
9.112 Greenwave Systems 308
9.113 GridGain Systems 309
9.114 H2O.ai 310
9.115 HarperDB 311
9.116 Hedvig 312
9.117 Hitachi Vantara 313
9.118 Hortonworks 314
9.119 HPE (Hewlett Packard Enterprise) 315
9.120 Huawei 317
9.121 HVR 318
9.122 HyperScience 319
9.123 HyTrust 320
9.124 IBM Corporation 322
9.125 iDashboards 324
9.126 IDERA 325
9.127 Ignite Technologies 326
9.128 Imanis Data 328
9.129 Impetus Technologies 329
9.130 Incorta 330
9.131 InetSoft Technology Corporation 331
9.132 InfluxData 332
9.133 Infogix 333
9.134 Infor/Birst 334
9.135 Informatica 336
9.136 Information Builders 337
9.137 Infosys 338
9.138 Infoworks 339
9.139 Insightsoftware.com 340
9.140 InsightSquared 341
9.141 Intel Corporation 342
9.142 Interana 343
9.143 InterSystems Corporation 344
9.144 Jedox 345
9.145 Jethro 346
9.146 Jinfonet Software 347
9.147 Juniper Networks 348
9.148 KALEAO 349
9.149 Keen IO 350
9.150 Keyrus 351
9.151 Kinetica 352
9.152 KNIME 353
9.153 Kognitio 354
9.154 Kyvos Insights 355
9.155 LeanXcale 356
9.156 Lexalytics 357
9.157 Lexmark International 359
9.158 Lightbend 360
9.159 Logi Analytics 361
9.160 Logical Clocks 362
9.161 Longview Solutions/Tidemark 363
9.162 Looker Data Sciences 365
9.163 LucidWorks 366
9.164 Luminoso Technologies 367
9.165 Maana 368
9.166 Manthan Software Services 369
9.167 MapD Technologies 370
9.168 MapR Technologies 371
9.169 MariaDB Corporation 372
9.170 MarkLogic Corporation 373
9.171 Mathworks 374
9.172 Melissa 375
9.173 MemSQL 376
9.174 Metric Insights 377
9.175 Microsoft Corporation 378
9.176 MicroStrategy 380
9.177 Minitab 381
9.178 MongoDB 382
9.179 Mu Sigma 383
9.180 NEC Corporation 384
9.181 Neo4j 385
9.182 NetApp 386
9.183 Nimbix 387
9.184 Nokia 388
9.185 NTT Data Corporation 389
9.186 Numerify 390
9.187 NuoDB 391
9.188 NVIDIA Corporation 392
9.189 Objectivity 393
9.190 Oblong Industries 394
9.191 OpenText Corporation 395
9.192 Opera Solutions 397
9.193 Optimal Plus 398
9.194 Oracle Corporation 399
9.195 Palantir Technologies 402
9.196 Panasonic Corporation/Arimo 404
9.197 Panorama Software 405
9.198 Paxata 406
9.199 Pepperdata 407
9.200 Phocas Software 408
9.201 Pivotal Software 409
9.202 Prognoz 411
9.203 Progress Software Corporation 412
9.204 Provalis Research 413
9.205 Pure Storage 414
9.206 PwC (PricewaterhouseCoopers International) 415
9.207 Pyramid Analytics 416
9.208 Qlik 417
9.209 Qrama/Tengu 418
9.210 Quantum Corporation 419
9.211 Qubole 420
9.212 Rackspace 421
9.213 Radius Intelligence 422
9.214 RapidMiner 423
9.215 Recorded Future 424
9.216 Red Hat 425
9.217 Redis Labs 426
9.218 RedPoint Global 427
9.219 Reltio 428
9.220 RStudio 429
9.221 Rubrik/Datos IO 430
9.222 Ryft 431
9.223 Sailthru 432
9.224 Salesforce.com 433
9.225 Salient Management Company 434
9.226 Samsung Group 435
9.227 SAP 436
9.228 SAS Institute 437
9.229 ScaleOut Software 438
9.230 Seagate Technology 439
9.231 Sinequa 440
9.232 SiSense 441
9.233 Sizmek 442
9.234 SnapLogic 443
9.235 Snowflake Computing 444
9.236 Software AG 445
9.237 Splice Machine 446
9.238 Splunk 447
9.239 Strategy Companion Corporation 449
9.240 Stratio 450
9.241 Streamlio 451
9.242 StreamSets 452
9.243 Striim 453
9.244 Sumo Logic 454
9.245 Supermicro (Super Micro Computer) 455
9.246 Syncsort 456
9.247 SynerScope 458
9.248 SYNTASA 459
9.249 Tableau Software 460
9.250 Talend 461
9.251 Tamr 462
9.252 TARGIT 463
9.253 TCS (Tata Consultancy Services) 464
9.254 Teradata Corporation 465
9.255 Thales/Guavus 467
9.256 ThoughtSpot 468
9.257 TIBCO Software 469
9.258 Toshiba Corporation 471
9.259 Transwarp 472
9.260 Trifacta 473
9.261 Unifi Software 474
9.262 Unravel Data 475
9.263 VANTIQ 476
9.264 VMware 477
9.265 VoltDB 478
9.266 WANdisco 479
9.267 Waterline Data 480
9.268 Western Digital Corporation 481
9.269 WhereScape 482
9.270 WiPro 483
9.271 Wolfram Research 484
9.272 Workday 486
9.273 Xplenty 488
9.274 Yellowfin BI 489
9.275 Yseop 490
9.276 Zendesk 491
9.277 Zoomdata 492
9.278 Zucchetti 493

10 Chapter 10: Conclusion & Strategic Recommendations 494
10.1 Why is the Market Poised to Grow? 494
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential? 494
10.3 Partnerships & M&A Activity: Highlighting the Importance of Big Data 495
10.4 The Significance of Edge Analytics for Automotive Applications 496
10.5 Achieving Customer Retention with Data-Driven Services 497
10.6 Addressing Privacy Concerns 497
10.7 The Role of Legislation 498
10.8 Encouraging Data Sharing in the Automotive Industry 499
10.9 Assessing the Impact of Self-Driving Vehicles 499
10.10 Recommendations 500
10.10.1 Big Data Hardware, Software & Professional Services Providers 500
10.10.2 Automotive OEMS & Other Stakeholders 501

List of Chart

Figure 1: Hadoop Architecture 40
Figure 2: Reactive vs. Proactive Analytics 51
Figure 3: Distribution of Big Data Investments in the Automotive Industry, by Application Area: 2018 (%) 58
Figure 4: Autonomous Vehicle Generated Data Volume by Sensor (%) 65
Figure 5: On-Board Sensors in an Autonomous Vehicle 66
Figure 6: Audi's Enterprise Big Data Platform 73
Figure 7: Toyota's Smart Center Architecture 95
Figure 8: Progressive Corporation's Use of Big Data for Automotive Insurance 112
Figure 9: Big Data Roadmap in the Automotive Industry: 2018 – 2030 119
Figure 10: Big Data Value Chain in the Automotive Industry 122
Figure 11: Key Aspects of Big Data Standardization 133
Figure 12: Global Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 146
Figure 13: Global Big Data Revenue in the Automotive Industry, by Hardware, Software & Professional Services: 2018 – 2030 ($ Million) 147
Figure 14: Global Big Data Revenue in the Automotive Industry, by Submarket: 2018 – 2030 ($ Million) 148
Figure 15: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 148
Figure 16: Global Big Data Networking Infrastructure Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 149
Figure 17: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 149
Figure 18: Global Big Data SQL Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 150
Figure 19: Global Big Data NoSQL Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 150
Figure 20: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 151
Figure 21: Global Big Data Cloud Platforms Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 151
Figure 22: Global Big Data Professional Services Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 152
Figure 23: Global Big Data Revenue in the Automotive Industry, by Application Area: 2018 – 2030 ($ Million) 153
Figure 24: Global Big Data Revenue in Automotive Product Development, Manufacturing & Supply Chain: 2018 – 2030 ($ Million) 153
Figure 25: Global Big Data Revenue in Automotive After-Sales, Warranty & Dealer Management: 2018 – 2030 ($ Million) 154
Figure 26: Global Big Data Revenue in Connected Vehicles & Intelligent Transportation: 2018 – 2030 ($ Million) 154
Figure 27: Global Big Data Revenue in Automotive Marketing, Sales & Other Applications: 2018 – 2030 ($ Million) 155
Figure 28: Global Big Data Revenue in the Automotive Industry, by Use Case: 2018 – 2030 ($ Million) 156
Figure 29: Global Big Data Revenue in Automotive Supply Chain Management: 2018 – 2030 ($ Million) 157
Figure 30: Global Big Data Revenue in Automotive Manufacturing: 2018 – 2030 ($ Million) 157
Figure 31: Global Big Data Revenue in Automotive Product Design & Planning: 2018 – 2030 ($ Million) 158
Figure 32: Global Big Data Revenue in Automotive Predictive Maintenance & Real-Time Diagnostics: 2018 – 2030 ($ Million) 158
Figure 33: Global Big Data Revenue in Automotive Recall & Warranty Management: 2018 – 2030 ($ Million) 159
Figure 34: Global Big Data Revenue in Automotive Parts Inventory & Pricing Optimization: 2018 – 2030 ($ Million) 159
Figure 35: Global Big Data Revenue in Automotive Dealer Management & Customer Support Services: 2018 – 2030 ($ Million) 160
Figure 36: Global Big Data Revenue in UBI (Usage-Based Insurance): 2018 – 2030 ($ Million) 160
Figure 37: Global Big Data Revenue in Autonomous & Semi-Autonomous Driving: 2018 – 2030 ($ Million) 161
Figure 38: Global Big Data Revenue in Intelligent Transportation: 2018 – 2030 ($ Million) 161
Figure 39: Global Big Data Revenue in Fleet Management: 2018 – 2030 ($ Million) 162
Figure 40: Global Big Data Revenue in Driver Safety & Vehicle Cyber Security: 2018 – 2030 ($ Million) 162
Figure 41: Global Big Data Revenue in In-Vehicle Experience, Navigation & Infotainment: 2018 – 2030 ($ Million) 163
Figure 42: Global Big Data Revenue in Ride Sourcing, Sharing & Rentals: 2018 – 2030 ($ Million) 163
Figure 43: Global Big Data Revenue in Automotive Marketing & Sales: 2018 – 2030 ($ Million) 164
Figure 44: Global Big Data Revenue in Automotive Customer Retention: 2018 – 2030 ($ Million) 164
Figure 45: Global Big Data Revenue in Automotive Third Party Monetization: 2018 – 2030 ($ Million) 165
Figure 46: Global Big Data Revenue in Other Automotive Industry Use Cases: 2018 – 2030 ($ Million) 165
Figure 47: Big Data Revenue in the Automotive Industry, by Region: 2018 – 2030 ($ Million) 166
Figure 48: Asia Pacific Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 166
Figure 49: Asia Pacific Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) 167
Figure 50: Australia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 167
Figure 51: China Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 168
Figure 52: India Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 168
Figure 53: Indonesia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 169
Figure 54: Japan Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 169
Figure 55: Malaysia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 170
Figure 56: Pakistan Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 170
Figure 57: Philippines Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 171
Figure 58: Singapore Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 171
Figure 59: South Korea Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 172
Figure 60: Taiwan Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 172
Figure 61: Thailand Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 173
Figure 62: Rest of Asia Pacific Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 173
Figure 63: Eastern Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 174
Figure 64: Eastern Europe Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) 174
Figure 65: Czech Republic Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 175
Figure 66: Poland Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 175
Figure 67: Russia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 176
Figure 68: Rest of Eastern Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 176
Figure 69: Latin & Central America Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 177
Figure 70: Latin & Central America Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) 177
Figure 71: Argentina Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 178
Figure 72: Brazil Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 178
Figure 73: Mexico Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 179
Figure 74: Rest of Latin & Central America Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 179
Figure 75: Middle East & Africa Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 180
Figure 76: Middle East & Africa Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) 180
Figure 77: Israel Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 181
Figure 78: Qatar Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 181
Figure 79: Saudi Arabia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 182
Figure 80: South Africa Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 182
Figure 81: UAE Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 183
Figure 82: Rest of the Middle East & Africa Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 183
Figure 83: North America Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 184
Figure 84: North America Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) 184
Figure 85: Canada Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 185
Figure 86: USA Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 185
Figure 87: Western Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 186
Figure 88: Western Europe Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) 186
Figure 89: Denmark Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 187
Figure 90: Finland Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 187
Figure 91: France Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 188
Figure 92: Germany Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 188
Figure 93: Italy Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 189
Figure 94: Netherlands Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 189
Figure 95: Norway Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 190
Figure 96: Spain Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 190
Figure 97: Sweden Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 191
Figure 98: UK Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 191
Figure 99: Rest of Western Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) 192

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