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


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“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems.

Amid the proliferation of real-time 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 Research 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.

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

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.

List of Companies Mentioned

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

Table of Contents

1 Chapter 1: Introduction 22
1.1 Executive Summary 22
1.2 Topics Covered 24
1.3 Forecast Segmentation 25
1.4 Key Questions Answered 28
1.5 Key Findings 29
1.6 Methodology 30
1.7 Target Audience 31
1.8 Companies & Organizations Mentioned 32

2 Chapter 2: An Overview of Big Data 36
2.1 What is Big Data? 36
2.2 Key Approaches to Big Data Processing 36
2.2.1 Hadoop 37
2.2.2 NoSQL 39
2.2.3 MPAD (Massively Parallel Analytic Databases) 39
2.2.4 In-Memory Processing 40
2.2.5 Stream Processing Technologies 40
2.2.6 Spark 41
2.2.7 Other Databases & Analytic Technologies 41
2.3 Key Characteristics of Big Data 42
2.3.1 Volume 42
2.3.2 Velocity 42
2.3.3 Variety 42
2.3.4 Value 43
2.4 Market Growth Drivers 44
2.4.1 Awareness of Benefits 44
2.4.2 Maturation of Big Data Platforms 44
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 45
2.4.4 Growth of Data Volume, Velocity & Variety 45
2.4.5 Vendor Commitments & Partnerships 45
2.4.6 Technology Trends Lowering Entry Barriers 46
2.5 Market Barriers 46
2.5.1 Lack of Analytic Specialists 46
2.5.2 Uncertain Big Data Strategies 46
2.5.3 Organizational Resistance to Big Data Adoption 47
2.5.4 Technical Challenges: Scalability & Maintenance 47
2.5.5 Security & Privacy Concerns 47

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 51
3.5 Technology & Implementation Approaches 51
3.5.1 Grid Computing 51
3.5.2 In-Database Processing 52
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 55
3.5.9 Graph Analytics 55
3.5.10 Social Media, IT & Telco Network Analytics 56

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 62
4.4.2.1 Predictive Maintenance & Real-Time Diagnostics 62
4.4.2.2 Streamlining Recalls & Warranty 62
4.4.2.3 Parts Inventory & Pricing Optimization 63
4.4.2.4 Dealer Management & Customer Support Services 63
4.4.3 Connected Vehicles & Intelligent Transportation 64
4.4.3.1 UBI (Usage-Based Insurance) 64
4.4.3.2 Autonomous & Semi-Autonomous Driving 64
4.4.3.3 Intelligent Transportation 65
4.4.3.4 Fleet Management 66
4.4.3.5 Driver Safety & Vehicle Cyber Security 66
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 68
4.4.4.4 Other Applications 69

5 Chapter 5: Automotive Industry Case Studies 70
5.1 Automotive OEMs 70
5.1.1 BMW: Eliminating Defects in New Vehicle Models with Big Data 70
5.1.2 Daimler: Ensuring Quality Assurance with Big Data 72
5.1.3 FCA (Fiat Chrysler Automobiles): Enhancing Dealer Management with Big Data 73
5.1.4 Ford Motor Company: Making Efficient Transportation Decisions with Big Data 74
5.1.5 GM (General Motors Company): Personalizing In-Vehicle Experience with Big Data 76
5.1.6 Groupe PSA: Reducing Industrial Energy Bills with Big Data 77
5.1.7 Groupe Renault: Boosting Driver Safety with Big Data 79
5.1.8 Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data 80
5.1.9 Hyundai Motor Company: Empowering Connected & Self-Driving Cars with Big Data 82
5.1.10 Jaguar Land Rover: Realizing Better & Cheaper Vehicle Designs with Big Data 83
5.1.11 Mazda Motor Corporation: Creating Better Engines with Big Data 84
5.1.12 Nissan Motor Company: Leveraging Big Data to Drive After-Sales Business Growth 85
5.1.13 SAIC Motor Corporation: Transforming Stressful Driving to Enjoyable Moments with Big Data 87
5.1.14 Subaru: Turbocharging Dealer Interaction with Big Data 88
5.1.15 Suzuki Motor Corporation: Accelerating Vehicle Design and Innovation with Big Data 89
5.1.16 Tesla: Achieving Customer Loyalty with Big Data 90
5.1.17 Toyota Motor Corporation: Powering Smart Cars with Big Data 91
5.1.18 Volkswagen Group: Transitioning to End-to-End Mobility Solutions with Big Data 93
5.1.19 Volvo Cars: Reducing Breakdowns and Failures with Big Data 95
5.2 Other Stakeholders 96
5.2.1 automotiveMastermind: Helping Automotive Dealerships Increase Sales with Big Data 96
5.2.2 Continental: Making Vehicles Safer with Big Data 97
5.2.3 Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data 98
5.2.4 Delphi Automotive: Monetizing Connected Vehicles with Big Data 99
5.2.5 Denso Corporation: Enabling Hazard Prediction with Big Data 100
5.2.6 HERE: Easing Traffic Congestion with Big Data 101
5.2.7 Lytx: Ensuring Road Safety with Big Data 102
5.2.8 Michelin: Optimizing Tire Manufacturing with Big Data 103
5.2.9 Robert Bosch: Empowering Fleet Management & Vehicle Insurance with Big Data 104
5.2.10 THTA (Tokyo Hire-Taxi Association): Making Connected Taxis a Reality with Big Data 105
5.2.11 Uber Technologies: Revolutionizing Ride Sourcing with Big Data 106

6 Chapter 6: Future Roadmap & Value Chain 108
6.1 Future Roadmap 108
6.1.1 2017 – 2020: Growing Investments in Real-Time & Predictive Analytics 108
6.1.2 2020 – 2025: Large-Scale Monetization of Automotive Big Data 109
6.1.3 2025 – 2030: Enabling Autonomous Driving & Future IoT Applications 109
6.2 Value Chain 110
6.2.1 Hardware Providers 110
6.2.1.1 Storage & Compute Infrastructure Providers 111
6.2.1.2 Networking Infrastructure Providers 111
6.2.2 Software Providers 112
6.2.2.1 Hadoop & Infrastructure Software Providers 112
6.2.2.2 SQL & NoSQL Providers 112
6.2.2.3 Analytic Platform & Application Software Providers 112
6.2.2.4 Cloud Platform Providers 113
6.2.3 Professional Services Providers 113
6.2.4 End-to-End Solution Providers 113
6.2.5 Automotive Industry 113

7 Chapter 7: Standardization & Regulatory Initiatives 114
7.1 ASF (Apache Software Foundation) 114
7.1.1 Management of Hadoop 114
7.1.2 Big Data Projects Beyond Hadoop 114
7.2 CSA (Cloud Security Alliance) 117
7.2.1 BDWG (Big Data Working Group) 117
7.3 CSCC (Cloud Standards Customer Council) 118
7.3.1 Big Data Working Group 118
7.4 DMG (Data Mining Group) 119
7.4.1 PMML (Predictive Model Markup Language) Working Group 119
7.4.2 PFA (Portable Format for Analytics) Working Group 119
7.5 IEEE (Institute of Electrical and Electronics Engineers) 120
7.5.1 Big Data Initiative 120
7.6 INCITS (InterNational Committee for Information Technology Standards) 121
7.6.1 Big Data Technical Committee 121
7.7 ISO (International Organization for Standardization) 122
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange 122
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms 123
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques 123
7.7.4 ISO/IEC JTC 1/WG 9: Big Data 123
7.7.5 Collaborations with Other ISO Work Groups 125
7.8 ITU (International Telecommunications Union) 125
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities 125
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks 126
7.8.3 Other Relevant Work 127
7.9 Linux Foundation 127
7.9.1 ODPi (Open Ecosystem of Big Data) 127
7.10 NIST (National Institute of Standards and Technology) 128
7.10.1 NBD-PWG (NIST Big Data Public Working Group) 128
7.11 OASIS (Organization for the Advancement of Structured Information Standards) 129
7.11.1 Technical Committees 129
7.12 ODaF (Open Data Foundation) 130
7.12.1 Big Data Accessibility 130
7.13 ODCA (Open Data Center Alliance) 130
7.13.1 Work on Big Data 130
7.14 OGC (Open Geospatial Consortium) 131
7.14.1 Big Data DWG (Domain Working Group) 131
7.15 TM Forum 131
7.15.1 Big Data Analytics Strategic Program 131
7.16 TPC (Transaction Processing Performance Council) 132
7.16.1 TPC-BDWG (TPC Big Data Working Group) 132
7.17 W3C (World Wide Web Consortium) 132
7.17.1 Big Data Community Group 132
7.17.2 Open Government Community Group 133

8 Chapter 8: Market Analysis & Forecasts 134
8.1 Global Outlook for Big Data in the Automotive Industry 134
8.2 Hardware, Software & Professional Services Segmentation 135
8.3 Horizontal Submarket Segmentation 136
8.4 Hardware Submarkets 136
8.4.1 Storage and Compute Infrastructure 136
8.4.2 Networking Infrastructure 137
8.5 Software Submarkets 137
8.5.1 Hadoop & Infrastructure Software 137
8.5.2 SQL 138
8.5.3 NoSQL 138
8.5.4 Analytic Platforms & Applications 139
8.5.5 Cloud Platforms 139
8.6 Professional Services Submarket 140
8.6.1 Professional Services 140
8.7 Application Area Segmentation 141
8.7.1 Product Development, Manufacturing & Supply Chain 141
8.7.2 After-Sales, Warranty & Dealer Management 142
8.7.3 Connected Vehicles & Intelligent Transportation 142
8.7.4 Marketing, Sales & Other Applications 143
8.8 Use Case Segmentation 144
8.9 Product Development, Manufacturing & Supply Chain Use Cases 145
8.9.1 Supply Chain Management 145
8.9.2 Manufacturing 145
8.9.3 Product Design & Planning 146
8.10 After-Sales, Warranty & Dealer Management Use Cases 146
8.10.1 Predictive Maintenance & Real-Time Diagnostics 146
8.10.2 Recall & Warranty Management 147
8.10.3 Parts Inventory & Pricing Optimization 147
8.10.4 Dealer Management & Customer Support Services 148
8.11 Connected Vehicles & Intelligent Transportation Use Cases 148
8.11.1 UBI (Usage-Based Insurance) 148
8.11.2 Autonomous & Semi-Autonomous Driving 149
8.11.3 Intelligent Transportation 149
8.11.4 Fleet Management 150
8.11.5 Driver Safety & Vehicle Cyber Security 150
8.11.6 In-Vehicle Experience, Navigation & Infotainment 151
8.11.7 Ride Sourcing, Sharing & Rentals 151
8.12 Marketing, Sales & Other Application Use Cases 152
8.12.1 Marketing & Sales 152
8.12.2 Customer Retention 152
8.12.3 Third Party Monetization 153
8.12.4 Other Use Cases 153
8.13 Regional Outlook 154
8.14 Asia Pacific 154
8.14.1 Country Level Segmentation 155
8.14.2 Australia 155
8.14.3 China 156
8.14.4 India 156
8.14.5 Indonesia 157
8.14.6 Japan 157
8.14.7 Malaysia 158
8.14.8 Pakistan 158
8.14.9 Philippines 159
8.14.10 Singapore 159
8.14.11 South Korea 160
8.14.12 Taiwan 160
8.14.13 Thailand 161
8.14.14 Rest of Asia Pacific 161
8.15 Eastern Europe 162
8.15.1 Country Level Segmentation 162
8.15.2 Czech Republic 163
8.15.3 Poland 163
8.15.4 Russia 164
8.15.5 Rest of Eastern Europe 164
8.16 Latin & Central America 165
8.16.1 Country Level Segmentation 165
8.16.2 Argentina 166
8.16.3 Brazil 166
8.16.4 Mexico 167
8.16.5 Rest of Latin & Central America 167
8.17 Middle East & Africa 168
8.17.1 Country Level Segmentation 168
8.17.2 Israel 169
8.17.3 Qatar 169
8.17.4 Saudi Arabia 170
8.17.5 South Africa 170
8.17.6 UAE 171
8.17.7 Rest of the Middle East & Africa 171
8.18 North America 172
8.18.1 Country Level Segmentation 172
8.18.2 Canada 173
8.18.3 USA 173
8.19 Western Europe 174
8.19.1 Country Level Segmentation 174
8.19.2 Denmark 175
8.19.3 Finland 175
8.19.4 France 176
8.19.5 Germany 176
8.19.6 Italy 177
8.19.7 Netherlands 177
8.19.8 Norway 178
8.19.9 Spain 178
8.19.10 Sweden 179
8.19.11 UK 179
8.19.12 Rest of Western Europe 180

9 Chapter 9: Vendor Landscape 181
9.1 1010data 181
9.2 Absolutdata 182
9.3 Accenture 183
9.4 Actian Corporation 184
9.5 Adaptive Insights 185
9.6 Advizor Solutions 186
9.7 AeroSpike 187
9.8 AFS Technologies 188
9.9 Alation 189
9.10 Algorithmia 190
9.11 Alluxio 191
9.12 Alpine Data 192
9.13 Alteryx 193
9.14 AMD (Advanced Micro Devices) 194
9.15 Apixio 195
9.16 Arcadia Data 196
9.17 Arimo 197
9.18 ARM 198
9.19 AtScale 199
9.20 Attivio 200
9.21 Attunity 201
9.22 Automated Insights 202
9.23 AWS (Amazon Web Services) 203
9.24 Axiomatics 204
9.25 Ayasdi 205
9.26 Basho Technologies 206
9.27 BCG (Boston Consulting Group) 207
9.28 Bedrock Data 208
9.29 BetterWorks 209
9.30 Big Cloud Analytics 210
9.31 BigML 211
9.32 Big Panda 212
9.33 Birst 213
9.34 Bitam 214
9.35 Blue Medora 215
9.36 BlueData Software 216
9.37 BlueTalon 217
9.38 BMC Software 218
9.39 BOARD International 219
9.40 Booz Allen Hamilton 220
9.41 Boxever 221
9.42 CACI International 222
9.43 Cambridge Semantics 223
9.44 Capgemini 224
9.45 Cazena 225
9.46 Centrifuge Systems 226
9.47 CenturyLink 227
9.48 Chartio 228
9.49 Cisco Systems 229
9.50 Civis Analytics 230
9.51 ClearStory Data 231
9.52 Cloudability 232
9.53 Cloudera 233
9.54 Clustrix 234
9.55 CognitiveScale 235
9.56 Collibra 236
9.57 Concurrent Computer Corporation 237
9.58 Confluent 238
9.59 Contexti 239
9.60 Continuum Analytics 240
9.61 Couchbase 241
9.62 CrowdFlower 242
9.63 Databricks 243
9.64 DataGravity 244
9.65 Dataiku 245
9.66 Datameer 246
9.67 DataRobot 247
9.68 DataScience 248
9.69 DataStax 249
9.70 DataTorrent 250
9.71 Datawatch Corporation 251
9.72 Datos IO 252
9.73 DDN (DataDirect Networks) 253
9.74 Decisyon 254
9.75 Dell Technologies 255
9.76 Deloitte 256
9.77 Demandbase 257
9.78 Denodo Technologies 258
9.79 Digital Reasoning Systems 259
9.80 Dimensional Insight 260
9.81 Dolphin Enterprise Solutions Corporation 261
9.82 Domino Data Lab 262
9.83 Domo 263
9.84 DriveScale 264
9.85 Dundas Data Visualization 265
9.86 DXC Technology 266
9.87 Eligotech 267
9.88 Engineering Group (Engineering Ingegneria Informatica) 268
9.89 EnterpriseDB 269
9.90 eQ Technologic 270
9.91 Ericsson 271
9.92 EXASOL 272
9.93 Facebook 273
9.94 FICO (Fair Isaac Corporation) 274
9.95 Fractal Analytics 275
9.96 Fujitsu 276
9.97 Fuzzy Logix 278
9.98 Gainsight 279
9.99 GE (General Electric) 280
9.100 Glassbeam 281
9.101 GoodData Corporation 282
9.102 Google 283
9.103 Greenwave Systems 284
9.104 GridGain Systems 285
9.105 Guavus 286
9.106 H2O.ai 287
9.107 HDS (Hitachi Data Systems) 288
9.108 Hedvig 289
9.109 Hortonworks 290
9.110 HPE (Hewlett Packard Enterprise) 291
9.111 Huawei 293
9.112 IBM Corporation 294
9.113 iDashboards 296
9.114 Impetus Technologies 297
9.115 Incorta 298
9.116 InetSoft Technology Corporation 299
9.117 Infer 300
9.118 Infor 301
9.119 Informatica Corporation 302
9.120 Information Builders 303
9.121 Infosys 304
9.122 Infoworks 305
9.123 Insightsoftware.com 306
9.124 InsightSquared 307
9.125 Intel Corporation 308
9.126 Interana 309
9.127 InterSystems Corporation 310
9.128 Jedox 311
9.129 Jethro 312
9.130 Jinfonet Software 313
9.131 Juniper Networks 314
9.132 KALEAO 315
9.133 Keen IO 316
9.134 Kinetica 317
9.135 KNIME 318
9.136 Kognitio 319
9.137 Kyvos Insights 320
9.138 Lavastorm 321
9.139 Lexalytics 322
9.140 Lexmark International 323
9.141 Logi Analytics 324
9.142 Longview Solutions 325
9.143 Looker Data Sciences 326
9.144 LucidWorks 327
9.145 Luminoso Technologies 328
9.146 Maana 329
9.147 Magento Commerce 330
9.148 Manthan Software Services 331
9.149 MapD Technologies 332
9.150 MapR Technologies 333
9.151 MariaDB Corporation 334
9.152 MarkLogic Corporation 335
9.153 Mathworks 336
9.154 MemSQL 337
9.155 Metric Insights 338
9.156 Microsoft Corporation 339
9.157 MicroStrategy 340
9.158 Minitab 341
9.159 MongoDB 342
9.160 Mu Sigma 343
9.161 NEC Corporation 344
9.162 Neo Technology 345
9.163 NetApp 346
9.164 Nimbix 347
9.165 Nokia 348
9.166 NTT Data Corporation 349
9.167 Numerify 350
9.168 NuoDB 351
9.169 Nutonian 352
9.170 NVIDIA Corporation 353
9.171 Oblong Industries 354
9.172 OpenText Corporation 355
9.173 Opera Solutions 357
9.174 Optimal Plus 358
9.175 Oracle Corporation 359
9.176 Palantir Technologies 361
9.177 Panorama Software 362
9.178 Paxata 363
9.179 Pentaho Corporation 364
9.180 Pepperdata 365
9.181 Phocas Software 366
9.182 Pivotal Software 367
9.183 Prognoz 369
9.184 Progress Software Corporation 370
9.185 PwC (PricewaterhouseCoopers International) 371
9.186 Pyramid Analytics 372
9.187 Qlik 373
9.188 Quantum Corporation 374
9.189 Qubole 375
9.190 Rackspace 376
9.191 Radius Intelligence 377
9.192 RapidMiner 378
9.193 Recorded Future 379
9.194 Red Hat 380
9.195 Redis Labs 381
9.196 RedPoint Global 382
9.197 Reltio 383
9.198 Rocket Fuel 384
9.199 RStudio 385
9.200 Ryft Systems 386
9.201 Sailthru 387
9.202 Salesforce.com 388
9.203 Salient Management Company 389
9.204 Samsung Group 390
9.205 SAP 391
9.206 SAS Institute 392
9.207 ScaleDB 393
9.208 ScaleOut Software 394
9.209 SCIO Health Analytics 395
9.210 Seagate Technology 396
9.211 Sinequa 397
9.212 SiSense 398
9.213 SnapLogic 399
9.214 Snowflake Computing 400
9.215 Software AG 401
9.216 Splice Machine 402
9.217 Splunk 403
9.218 Sqrrl 404
9.219 Strategy Companion Corporation 405
9.220 StreamSets 406
9.221 Striim 407
9.222 Sumo Logic 408
9.223 Supermicro (Super Micro Computer) 409
9.224 Syncsort 410
9.225 SynerScope 411
9.226 Tableau Software 412
9.227 Talena 413
9.228 Talend 414
9.229 Tamr 415
9.230 TARGIT 416
9.231 TCS (Tata Consultancy Services) 417
9.232 Teradata Corporation 418
9.233 ThoughtSpot 420
9.234 TIBCO Software 421
9.235 Tidemark 422
9.236 Toshiba Corporation 423
9.237 Trifacta 424
9.238 Unravel Data 425
9.239 VMware 426
9.240 VoltDB 427
9.241 Waterline Data 428
9.242 Western Digital Corporation 429
9.243 WiPro 430
9.244 Workday 431
9.245 Xplenty 432
9.246 Yellowfin International 433
9.247 Yseop 434
9.248 Zendesk 435
9.249 Zoomdata 436
9.250 Zucchetti 437

10 Chapter 10: Conclusion & Strategic Recommendations 438
10.1 Why is the Market Poised to Grow? 438
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential? 438
10.3 Partnerships & M&A Activity: Highlighting the Importance of Big Data 439
10.4 Achieving Customer Retention with Data-Driven Services 440
10.5 Addressing Privacy Concerns 440
10.6 The Role of Legislation 441
10.7 Encouraging Data Sharing in the Automotive Industry 442
10.8 Assessing the Impact of Self-Driving Vehicles 442
10.9 Recommendations 443
10.9.1 Big Data Hardware, Software & Professional Services Providers 443
10.9.2 Automotive OEMS & Other Stakeholders 444

Figure 1: Hadoop Architecture 38
Figure 2: Reactive vs. Proactive Analytics 51
Figure 3: Distribution of Big Data Investments in the Automotive Industry, by Application Area: 2016 (%) 58
Figure 4: Sensors in an Autonomous Vehicle 66
Figure 5: Toyota Smart Center Architecture 92
Figure 6: Big Data Roadmap in the Automotive Industry 109
Figure 7: Big Data Value Chain in the Automotive Industry 111
Figure 8: Key Aspects of Big Data Standardization 121
Figure 9: Global Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 135
Figure 10: Global Big Data Revenue in the Automotive Industry, by Hardware, Software & Professional Services: 2017 - 2030 ($ Million) 136
Figure 11: Global Big Data Revenue in the Automotive Industry, by Submarket: 2017 - 2030 ($ Million) 137
Figure 12: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 137
Figure 13: Global Big Data Networking Infrastructure Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 138
Figure 14: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 138
Figure 15: Global Big Data SQL Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 139
Figure 16: Global Big Data NoSQL Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 139
Figure 17: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 140
Figure 18: Global Big Data Cloud Platforms Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 140
Figure 19: Global Big Data Professional Services Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 141
Figure 20: Global Big Data Revenue in the Automotive Industry, by Application Area: 2017 - 2030 ($ Million) 142
Figure 21: Global Big Data Revenue in Automotive Product Development, Manufacturing & Supply Chain: 2017 - 2030 ($ Million) 142
Figure 22: Global Big Data Revenue in Automotive After-Sales, Warranty & Dealer Management: 2017 - 2030 ($ Million) 143
Figure 23: Global Big Data Revenue in Connected Vehicles & Intelligent Transportation: 2017 - 2030 ($ Million) 143
Figure 24: Global Big Data Revenue in Automotive Marketing, Sales & Other Applications: 2017 - 2030 ($ Million) 144
Figure 25: Global Big Data Revenue in the Automotive Industry, by Use Case: 2017 - 2030 ($ Million) 145
Figure 26: Global Big Data Revenue in Automotive Supply Chain Management: 2017 - 2030 ($ Million) 146
Figure 27: Global Big Data Revenue in Automotive Manufacturing: 2017 - 2030 ($ Million) 146
Figure 28: Global Big Data Revenue in Automotive Product Design & Planning: 2017 - 2030 ($ Million) 147
Figure 29: Global Big Data Revenue in Automotive Predictive Maintenance & Real-Time Diagnostics: 2017 - 2030 ($ Million) 147
Figure 30: Global Big Data Revenue in Automotive Recall & Warranty Management: 2017 - 2030 ($ Million) 148
Figure 31: Global Big Data Revenue in Automotive Parts Inventory & Pricing Optimization: 2017 - 2030 ($ Million) 148
Figure 32: Global Big Data Revenue in Automotive Dealer Management & Customer Support Services: 2017 - 2030 ($ Million) 149
Figure 33: Global Big Data Revenue in UBI (Usage-Based Insurance): 2017 - 2030 ($ Million) 149
Figure 34: Global Big Data Revenue in Autonomous & Semi-Autonomous Driving: 2017 - 2030 ($ Million) 150
Figure 35: Global Big Data Revenue in Intelligent Transportation: 2017 - 2030 ($ Million) 150
Figure 36: Global Big Data Revenue in Fleet Management: 2017 - 2030 ($ Million) 151
Figure 37: Global Big Data Revenue in Driver Safety & Vehicle Cyber Security: 2017 - 2030 ($ Million) 151
Figure 38: Global Big Data Revenue in In-Vehicle Experience, Navigation & Infotainment: 2017 - 2030 ($ Million) 152
Figure 39: Global Big Data Revenue in Ride Sourcing, Sharing & Rentals: 2017 - 2030 ($ Million) 152
Figure 40: Global Big Data Revenue in Automotive Marketing & Sales: 2017 - 2030 ($ Million) 153
Figure 41: Global Big Data Revenue in Automotive Customer Retention: 2017 - 2030 ($ Million) 153
Figure 42: Global Big Data Revenue in Automotive Third Party Monetization: 2017 - 2030 ($ Million) 154
Figure 43: Global Big Data Revenue in Other Automotive Industry Use Cases: 2017 - 2030 ($ Million) 154
Figure 44: Big Data Revenue in the Automotive Industry, by Region: 2017 - 2030 ($ Million) 155
Figure 45: Asia Pacific Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 155
Figure 46: Asia Pacific Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) 156
Figure 47: Australia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 156
Figure 48: China Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 157
Figure 49: India Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 157
Figure 50: Indonesia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 158
Figure 51: Japan Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 158
Figure 52: Malaysia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 159
Figure 53: Pakistan Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 159
Figure 54: Philippines Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 160
Figure 55: Singapore Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 160
Figure 56: South Korea Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 161
Figure 57: Taiwan Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 161
Figure 58: Thailand Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 162
Figure 59: Rest of Asia Pacific Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 162
Figure 60: Eastern Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 163
Figure 61: Eastern Europe Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) 163
Figure 62: Czech Republic Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 164
Figure 63: Poland Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 164
Figure 64: Russia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 165
Figure 65: Rest of Eastern Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 165
Figure 66: Latin & Central America Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 166
Figure 67: Latin & Central America Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) 166
Figure 68: Argentina Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 167
Figure 69: Brazil Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 167
Figure 70: Mexico Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 168
Figure 71: Rest of Latin & Central America Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 168
Figure 72: Middle East & Africa Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 169
Figure 73: Middle East & Africa Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) 169
Figure 74: Israel Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 170
Figure 75: Qatar Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 170
Figure 76: Saudi Arabia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 171
Figure 77: South Africa Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 171
Figure 78: UAE Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 172
Figure 79: Rest of the Middle East & Africa Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 172
Figure 80: North America Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 173
Figure 81: North America Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) 173
Figure 82: Canada Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 174
Figure 83: USA Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 174
Figure 84: Western Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 175
Figure 85: Western Europe Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) 175
Figure 86: Denmark Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 176
Figure 87: Finland Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 176
Figure 88: France Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 177
Figure 89: Germany Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 177
Figure 90: Italy Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 178
Figure 91: Netherlands Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 178
Figure 92: Norway Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 179
Figure 93: Spain Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 179
Figure 94: Sweden Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 180
Figure 95: UK Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 180
Figure 96: Rest of Western Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) 181