Big Data In The Automotive Industry: 2017 – 2030 – Opportunities, Challenges, Strategies & Forecasts

“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.

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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
Big Data In The Automotive Industry

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
Big Data In The Automotive Industry

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?

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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

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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

Continue…

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