BIG DATA IN THE AUTOMOTIVE MARKET 2020 – IMPACT OF COVID-19, FUTURE GROWTH ANALYSIS AND CHALLENGES

“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.

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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

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Country Markets

Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK, USA

Key Questions Answered

The report provides answers to the following key questions:

How big is the Big Data opportunity in the automotive industry?
How is the market evolving by segment and region?
What will the market size be in 2021, and at what rate will it grow?
What trends, challenges and barriers are influencing its growth?
Who are the key Big Data software, hardware and services vendors, and what are their strategies?
How much are automotive OEMs and other stakeholders investing in Big Data?
What opportunities exist for Big Data analytics in the automotive industry?
Which countries, application areas and use cases will see the highest percentage of Big Data investments in the automotive industry?

Key Findings

The report has the following key findings:

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

List of Companies Mentioned

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

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