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

Published: Jul, 2018 | Pages: 501 | Publisher: SNS Research
Industry: ICT | Report Format: Electronic (PDF)

“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
•	EVŌ (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

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
		
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
		
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
		
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
		
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
		
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
		
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
		
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
		
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	EVŌ (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
		
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 Figures	
	
	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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