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


Report Details

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

SKU SNSJUL151801
Category Automotive
Publisher SNS Telecom
Pages 501
Published Jul-18
Request Discount Pay by Wire/Invoice

Description

Big Data investments in the automotive industry will account for more than $3.3 Billion in 2018 expected over $5 Billion by the end of 2021.
“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.

Research 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.


News/Press Release

Table of Content

Table of Contents
Chapter 1: Introduction
Executive Summary
Topics Covered
Forecast Segmentation
Key Questions Answered
Key Findings
Methodology
Target Audience
Companies & Organizations Mentioned

Chapter 2: An Overview of Big Data
What is Big Data?
Key Approaches to Big Data Processing
Hadoop
NoSQL
MPAD (Massively Parallel Analytic Databases)
In-Memory Processing
Stream Processing Technologies
Spark
Other Databases & Analytic Technologies
Key Characteristics of Big Data
Volume
Velocity
Variety
Value
Market Growth Drivers
Awareness of Benefits
Maturation of Big Data Platforms
Continued Investments by Web Giants, Governments & Enterprises
Growth of Data Volume, Velocity & Variety
Vendor Commitments & Partnerships
Technology Trends Lowering Entry Barriers
Market Barriers
Lack of Analytic Specialists
Uncertain Big Data Strategies
Organizational Resistance to Big Data Adoption
Technical Challenges: Scalability & Maintenance
Security & Privacy Concerns

Chapter 3: Big Data Analytics
What are Big Data Analytics?
The Importance of Analytics
Reactive vs. Proactive Analytics
Customer vs. Operational Analytics
Technology & Implementation Approaches
Grid Computing
In-Database Processing
In-Memory Analytics
Machine Learning & Data Mining
Predictive Analytics
NLP (Natural Language Processing)
Text Analytics
Visual Analytics
Graph Analytics
Social Media, IT & Telco Network Analytics

Chapter 4: Business Case & Applications in the Automotive Industry
Overview & Investment Potential
Industry Specific Market Growth Drivers
Industry Specific Market Barriers
Key Applications
Product Development, Manufacturing & Supply Chain
Optimizing the Supply Chain
Eliminating Manufacturing Defects
Customer-Driven Product Design & Planning
After-Sales, Warranty & Dealer Management
Predictive Maintenance & Real-Time Diagnostics
Streamlining Recalls & Warranty
Parts Inventory & Pricing Optimization
Dealer Management & Customer Support Services
Connected Vehicles & Intelligent Transportation
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 & Other Applications
Marketing & Sales
Customer Retention
Third Party Monetization
Other Applications

Chapter 5: Automotive Industry Case Studies
Automotive OEMs
Audi: Facilitating Efficient Production Processes with Big Data
BMW: Eliminating Defects in New Vehicle Models with Big Data
Daimler: Ensuring Quality Assurance with Big Data
Dongfeng Motor Corporation: Enriching Network-Connected Autonomous Vehicles with Big Data
FCA (Fiat Chrysler Automobiles): Enhancing Dealer Management with Big Data
Ford Motor Company: Making Efficient Transportation Decisions with Big Data
GM (General Motors Company): Personalizing In-Vehicle Experience with Big Data
Groupe PSA: Reducing Industrial Energy Bills with Big Data
Groupe Renault: Boosting Driver Safety with Big Data
Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data
Hyundai Motor Company: Empowering Connected & Self-Driving Cars with Big Data
Jaguar Land Rover: Realizing Better & Cheaper Vehicle Designs with Big Data
Mazda Motor Corporation: Creating Better Engines with Big Data
Nissan Motor Company: Leveraging Big Data to Drive After-Sales Business Growth
SAIC Motor Corporation: Transforming Stressful Driving to Enjoyable Moments with Big Data
Subaru: Turbocharging Dealer Interaction with Big Data
Suzuki Motor Corporation: Accelerating Vehicle Design and Innovation with Big Data
Tesla: Achieving Customer Loyalty with Big Data
Toyota Motor Corporation: Powering Smart Cars with Big Data
Volkswagen Group: Transitioning to End-to-End Mobility Solutions with Big Data
Volvo Cars: Reducing Breakdowns and Failures with Big Data
Other Stakeholders
Allstate Corporation & Arity: Making Transportation Safer & Smarter with Big Data
automotiveMastermind: Helping Automotive Dealerships Increase Sales with Big Data
Continental: Making Vehicles Safer with Big Data
Cox Automotive: Transforming the Used Vehicle Lifecycle with Big Data
Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data
Delphi Automotive: Monetizing Connected Vehicles with Big Data
Denso Corporation: Enabling Hazard Prediction with Big Data
HERE: Easing Traffic Congestion with Big Data
Lytx: Ensuring Road Safety with Big Data
Michelin: Optimizing Tire Manufacturing with Big Data
Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data
Bosch: Empowering Fleet Management & Vehicle Insurance with Big Data
THTA (Tokyo Hire-Taxi Association): Making Connected Taxis a Reality with Big Data
Uber Technologies: Revolutionizing Ride Sourcing with Big Data
U.S. Xpress: Driving Fuel-Savings with Big Data

Chapter 6: Future Roadmap & Value Chain
Future Roadmap
Pre-2020: Investments in Advanced Analytics for Vehicle-Related Services
2020 – 2025: Proliferation of Real-Time Edge Analytics & Automotive Data Monetization
2025 – 2030: Towards Fully Autonomous Driving & Future IoT Applications
The Big Data Value Chain
Hardware Providers
Storage & Compute Infrastructure Providers
Networking Infrastructure Providers
Software Providers
Hadoop & Infrastructure Software Providers
SQL & NoSQL Providers
Analytic Platform & Application Software Providers
Cloud Platform Providers
Professional Services Providers
End-to-End Solution Providers
Automotive Industry

Chapter 7: Standardization & Regulatory Initiatives
ASF (Apache Software Foundation)
Management of Hadoop
Big Data Projects Beyond Hadoop
CSA (Cloud Security Alliance)
BDWG (Big Data Working Group)
CSCC (Cloud Standards Customer Council)
Big Data Working Group
DMG (Data Mining Group)
PMML (Predictive Model Markup Language) Working Group
PFA (Portable Format for Analytics) Working Group
IEEE (Institute of Electrical and Electronics Engineers)
Big Data Initiative
INCITS (InterNational Committee for Information Technology Standards)
Big Data Technical Committee
ISO (International Organization for Standardization)
ISO/IEC JTC 1/SC 32: Data Management and Interchange
ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms
ISO/IEC JTC 1/SC 27: IT Security Techniques
ISO/IEC JTC 1/WG 9: Big Data
Collaborations with Other ISO Work Groups
ITU (International Telecommunication Union)
ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities
Other Deliverables Through SG (Study Group) 13 on Future Networks
Other Relevant Work
Linux Foundation
ODPi (Open Ecosystem of Big Data)
NIST (National Institute of Standards and Technology)
NBD-PWG (NIST Big Data Public Working Group)
OASIS (Organization for the Advancement of Structured Information Standards)
Technical Committees
ODaF (Open Data Foundation)
Big Data Accessibility
ODCA (Open Data Center Alliance)
Work on Big Data
OGC (Open Geospatial Consortium)
Big Data DWG (Domain Working Group)
TM Forum
Big Data Analytics Strategic Program
TPC (Transaction Processing Performance Council)
TPC-BDWG (TPC Big Data Working Group)
W3C (World Wide Web Consortium)
Big Data Community Group
Open Government Community Group

Chapter 8: Market Sizing & Forecasts
Global Outlook for Big Data in the Automotive Industry
Hardware, Software & Professional Services Segmentation
Horizontal Submarket Segmentation
Hardware Submarkets
Storage and Compute Infrastructure
Networking Infrastructure
Software Submarkets
Hadoop & Infrastructure Software
SQL
NoSQL
Analytic Platforms & Applications
Cloud Platforms
Professional Services Submarket
Professional Services
Application Area Segmentation
Product Development, Manufacturing & Supply Chain
After-Sales, Warranty & Dealer Management
Connected Vehicles & Intelligent Transportation
Marketing, Sales & Other Applications
Use Case Segmentation
Product Development, Manufacturing & Supply Chain Use Cases
Supply Chain Management
Manufacturing
Product Design & Planning
After-Sales, Warranty & Dealer Management Use Cases
Predictive Maintenance & Real-Time Diagnostics
Recall & Warranty Management
Parts Inventory & Pricing Optimization
Dealer Management & Customer Support Services
Connected Vehicles & Intelligent Transportation Use Cases
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 & Other Application Use Cases
Marketing & Sales
Customer Retention
Third Party Monetization
Other Use Cases
Regional Outlook
Asia Pacific
Country Level Segmentation
Australia
China
India
Indonesia
Japan
Malaysia
Pakistan
Philippines
Singapore
South Korea
Taiwan
Thailand
Rest of Asia Pacific
Eastern Europe
Country Level Segmentation
Czech Republic
Poland
Russia
Rest of Eastern Europe
Latin & Central America
Country Level Segmentation
Argentina
Brazil
Mexico
Rest of Latin & Central America
Middle East & Africa
Country Level Segmentation
Israel
Qatar
Saudi Arabia
South Africa
UAE
Rest of the Middle East & Africa
North America
Country Level Segmentation
Canada
USA
Western Europe
Country Level Segmentation
Denmark
Finland
France
Germany
Italy
Netherlands
Norway
Spain
Sweden
UK
Rest of Western Europe

Chapter 9: Vendor Landscape
1010data
Absolutdata
Accenture
Actian Corporation/HCL Technologies
Adaptive Insights
Adobe Systems
Advizor Solutions
AeroSpike
AFS Technologies
Alation
Algorithmia
Alluxio
ALTEN
Alteryx
AMD (Advanced Micro Devices)
Anaconda
Apixio
Arcadia Data
ARM
AtScale
Attivio
Attunity
Automated Insights
AVORA
AWS (Amazon Web Services)
Axiomatics
Ayasdi
BackOffice Associates
Basho Technologies
BCG (Boston Consulting Group)
Bedrock Data
BetterWorks
Big Panda
BigML
Bitam
Blue Medora
BlueData Software
BlueTalon
BMC Software
BOARD International
Booz Allen Hamilton
Boxever
CACI International
Cambridge Semantics
Capgemini
Cazena
Centrifuge Systems
CenturyLink
Chartio
Cisco Systems
Civis Analytics
ClearStory Data
Cloudability
Cloudera
Cloudian
Clustrix
CognitiveScale
Collibra
Concurrent Technology/Vecima Networks
Confluent
Contexti
Couchbase
Crate.io
Cray
Databricks
Dataiku
Datalytyx
Datameer
DataRobot
DataStax
Datawatch Corporation
DDN (DataDirect Networks)
Decisyon
Dell Technologies
Deloitte
Demandbase
Denodo Technologies
Dianomic Systems
Digital Reasoning Systems
Dimensional Insight
Dolphin Enterprise Solutions Corporation/Hanse Orga Group
Domino Data Lab
Domo
Dremio
DriveScale
Druva
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
FICO (Fair Isaac Corporation)
Figure Eight
FogHorn Systems
Fractal Analytics
Franz
Fujitsu
Fuzzy Logix
Gainsight
GE (General Electric)
Glassbeam
GoodData Corporation
Google/Alphabet
Grakn Labs
Greenwave Systems
GridGain Systems
H2O.ai
HarperDB
Hedvig
Hitachi Vantara
Hortonworks
HPE (Hewlett Packard Enterprise)
Huawei
HVR
HyperScience
HyTrust
IBM Corporation
iDashboards
IDERA
Ignite Technologies
Imanis Data
Impetus Technologies
Incorta
InetSoft Technology Corporation
InfluxData
Infogix
Infor/Birst
Informatica
Information Builders
Infosys
Infoworks
Insightsoftware.com
InsightSquared
Intel Corporation
Interana
InterSystems Corporation
Jedox
Jethro
Jinfonet Software
Juniper Networks
KALEAO
Keen IO
Keyrus
Kinetica
KNIME
Kognitio
Kyvos Insights
LeanXcale
Lexalytics
Lexmark International
Lightbend
Logi Analytics
Logical Clocks
Longview Solutions/Tidemark
Looker Data Sciences
LucidWorks
Luminoso Technologies
Maana
Manthan Software Services
MapD Technologies
MapR Technologies
MariaDB Corporation
MarkLogic Corporation
Mathworks
Melissa
MemSQL
Metric Insights
Microsoft Corporation
MicroStrategy
Minitab
MongoDB
Mu Sigma
NEC Corporation
Neo4j
NetApp
Nimbix
Nokia
NTT Data Corporation
Numerify
NuoDB
NVIDIA Corporation
Objectivity
Oblong Industries
OpenText Corporation
Opera Solutions
Optimal Plus
Oracle Corporation
Palantir Technologies
Panasonic Corporation/Arimo
Panorama Software
Paxata
Pepperdata
Phocas Software
Pivotal Software
Prognoz
Progress Software 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/Datos IO
Ryft
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
Sumo Logic
Supermicro (Super Micro Computer)
Syncsort
SynerScope
SYNTASA
Tableau Software
Talend
Tamr
TARGIT
TCS (Tata Consultancy Services)
Teradata Corporation
Thales/Guavus
ThoughtSpot
TIBCO Software
Toshiba Corporation
Transwarp
Trifacta
Unifi Software
Unravel Data
VANTIQ
VMware
VoltDB
WANdisco
Waterline Data
Western Digital Corporation
WhereScape
WiPro
Wolfram Research
Workday
Xplenty
Yellowfin BI
Yseop
Zendesk
Zoomdata
Zucchetti

Chapter 10: Conclusion & Strategic Recommendations
Why is the Market Poised to Grow?
Geographic Outlook: Which Countries Offer the Highest Growth Potential?
Partnerships & M&A Activity: Highlighting the Importance of Big Data
The Significance of Edge Analytics for Automotive Applications
Achieving Customer Retention with Data-Driven Services
Addressing Privacy Concerns
The Role of Legislation
Encouraging Data Sharing in the Automotive Industry
Assessing the Impact of Self-Driving Vehicles
Recommendations
Big Data Hardware, Software & Professional Services Providers
Automotive OEMS & Other Stakeholders

List of Figures

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

Companies Profiled

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

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