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

Published: May, 2017 | Pages: 445 | 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 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.

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, key applications and use cases in the automotive industry
 - 30 case studies of Big Data investments by automotive OEMs and other stakeholders
 - Future roadmap and value chain
 - Company profiles and strategies of over 240 Big Data vendors
 - Strategic recommendations for Big Data vendors, automotive OEMs and other stakeholders
 - Market analysis and forecasts from 2017 till 2030

Forecast Segmentation
Market forecasts are provided for each of the following submarkets and their subcategories:

Hardware, Software & Professional Services
 - Hardware
 - Software
 - Professional Services

Horizontal Submarkets
 - Storage & Compute Infrastructure
 - Networking Infrastructure
 - Hadoop & Infrastructure Software
 - SQL
 - NoSQL
 - Analytic Platforms & Applications
 - Cloud Platforms
 - Professional Services

Application Areas
 - Product Development, Manufacturing & Supply Chain
 - After-Sales, Warranty & Dealer Management
 - Connected Vehicles & Intelligent Transportation
 - Marketing, Sales & Other Applications

Use Cases
 - Supply Chain Management
 - Manufacturing
 - Product Design & Planning
 - Predictive Maintenance & Real-Time Diagnostics
 - Recall & Warranty Management
 - Parts Inventory & Pricing Optimization
 - Dealer Management & Customer Support Services
 - UBI (Usage-Based Insurance)
 - Autonomous & Semi-Autonomous Driving
 - Intelligent Transportation
 - Fleet Management
 - Driver Safety & Vehicle Cyber Security
 - In-Vehicle Experience, Navigation & Infotainment
 - Ride Sourcing, Sharing & Rentals
 - Marketing & Sales
 - Customer Retention
 - Third Party Monetization
 - Other Use Cases

Regional Markets
 - Asia Pacific
 - Eastern Europe
 - Latin & Central America
 - Middle East & Africa
 - North America
 - Western Europe

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

Key Questions Answered 
The report provides answers to the following key questions:
 - How big is the Big Data opportunity in the automotive industry?
 - How is the market evolving by segment and region?
 - What will the market size be in 2020 and at what rate will it grow?
 - What trends, challenges and barriers are influencing its growth?
 - Who are the key Big Data software, hardware and services vendors and what are their strategies?
 - How much are automotive OEMs and other stakeholders investing in Big Data?
 - What opportunities exist for Big Data analytics in the automotive industry?
 - Which countries, application areas and use cases will see the highest percentage of Big Data investments in the automotive industry?

Key Findings 
The report has the following key findings: 
 - In 2017, Big Data vendors will pocket over $2.8 Billion from hardware, software and professional services revenues in the automotive industry. These investments are further expected to grow at a CAGR of approximately 12% over the next three years, eventually accounting for over $4 Billion by the end of 2020.
 - In a bid to improve customer retention, automotive OEMs are heavily relying on Big Data and analytics to integrate an array of data-driven aftermarket services such as predictive vehicle maintenance, real-time mapping and personalized concierge services.
 - In recent years, several prominent partnerships and M&A deals have taken place that highlight the growing importance of Big Data in the automotive industry. For example, tier-1 supplier Delphi recently led an investment round to raise over $25 Million for Otonomo, a startup that has developed a data exchange and marketplace platform for vehicle-generated data.
 - Addressing privacy concerns is necessary in order to monetize the swaths of Big Data that will be generated by a growing installed base of connected vehicles and other segments of the automotive industry.

List of Companies Mentioned

1010data
Absolutdata
Accenture
ACEA (European Automobile Manufacturers’ Association)
Actian Corporation
Adaptive Insights
Advizor Solutions
AeroSpike
AFS Technologies
Alation
Algorithmia
Alibaba
Alliance of Automobile Manufacturers
Alluxio
Alphabet
Alpine Data
Alteryx
AMD (Advanced Micro Devices)
Apixio
Arcadia Data
Arimo
ARM
ASF (Apache Software Foundation)
AtScale
Attivio
Attunity
Audi
Automated Insights
automotiveMastermind
AWS (Amazon Web Services)
Axiomatics
Ayasdi
Basho Technologies
BCG (Boston Consulting Group)
Bedrock Data
BetterWorks
Big Cloud Analytics
Big Panda
BigML
Birst
Bitam
Blue Medora
BlueData Software
BlueTalon
BMC Software
BMW
BOARD International
Booz Allen Hamilton
Boxever
CACI International
Cambridge Semantics
Capgemini
Cazena
Centrifuge Systems
CenturyLink
Chartio
Cisco Systems
Civis Analytics
ClearStory Data
Cloudability
Cloudera
Clustrix
CognitiveScale
Collibra
Concurrent Computer Corporation
Confluent
Contexti
Continental
Continuum Analytics
Couchbase
CrowdFlower
CSA (Cloud Security Alliance)
CSCC (Cloud Standards Customer Council)
Daimler
Dash Labs
Databricks
DataGravity
Dataiku
Datameer
DataRobot
DataScience
DataStax
DataTorrent
Datawatch Corporation
Datos IO
DDN (DataDirect Networks)
Decisyon
Dell EMC
Dell Technologies
Deloitte
Delphi Automotive
Demandbase
Denodo Technologies
Denso Corporation
Digital Reasoning Systems
Dimensional Insight
DMG  (Data Mining Group)
Dolphin Enterprise Solutions Corporation
Domino Data Lab
Domo
DriveScale
Dundas Data Visualization
DXC Technology
Eligotech
Engie
Engineering Group (Engineering Ingegneria Informatica)
EnterpriseDB
eQ Technologic
Ericsson
EXASOL
Facebook
FCA (Fiat Chrysler Automobiles)
FICO (Fair Isaac Corporation)
Ford Motor Company
Fractal Analytics
FTC (U.S. Federal Trade Commission)
Fujitsu
Fuzzy Logix
Gainsight
GE (General Electric)
Geely (Zhejiang Geely Holding Group)
Glassbeam
GM (General Motors Company)
GoodData Corporation
Google
Greenwave Systems
GridGain Systems
Groupe PSA
Groupe Renault
Guavus
H2O.ai
HDS (Hitachi Data Systems)
Hedvig
HERE
Honda Motor Company
Hortonworks
HPE (Hewlett Packard Enterprise)
Huawei
Hyundai Motor Company
IBM Corporation
iDashboards
IEC (International Electrotechnical Commission)
IEEE (Institute of Electrical and Electronics Engineers)
Impetus Technologies
INCITS (InterNational Committee for Information Technology Standards)
Incorta
InetSoft Technology Corporation
Infer
Infor
Informatica Corporation
Information Builders
Infosys
Infoworks
Insightsoftware.com
InsightSquared
Intel Corporation
Interana
InterSystems Corporation
ISO (International Organization for Standardization)
Jaguar Land Rover
Jedox
Jethro
Jinfonet Software
Juniper Networks
KALEAO
KDDI Corporation
Keen IO
Kia Motor Corporation
Kinetica
KNIME
Kognitio
Kyvos Insights
Lavastorm
Lexalytics
Lexmark International
Lexus
Linux Foundation
Logi Analytics
Longview Solutions
Looker Data Sciences
LucidWorks
Luminoso Technologies
Lytx
Maana
Magento Commerce
Manthan Software Services
MapD Technologies
MapR Technologies
MariaDB Corporation
MarkLogic Corporation
Mathworks
Mazda Motor Corporation
MemSQL
Mercedes-Benz 
METI (Ministry of Economy, Trade and Industry, Japan) 
Metric Insights
Michelin
Microsoft Corporation
MicroStrategy
Minitab
MongoDB
Mu Sigma
NEC Corporation
Neo Technology
NetApp
Nimbix
Nissan Motor Company
NIST (U.S. National Institute of Standards and Technology)
Nokia
NTT Data Corporation
NTT Group
Numerify
NuoDB
Nutonian
NVIDIA Corporation
NYC DOT (New York City Department of Transportation)
OASIS (Organization for the Advancement of Structured Information Standards)
Oblong Industries
ODaF (Open Data Foundation)
ODCA (Open Data Center Alliance)
ODPi (Open Ecosystem of Big Data)
OGC (Open Geospatial Consortium)
OpenText Corporation
Opera Solutions
Optimal Plus
Oracle Corporation
Otonomo
Palantir Technologies
Panorama Software
Paxata
Pentaho Corporation
Pepperdata
Phocas Software
Pivotal Software
Prognoz
Progress Software Corporation
PwC (PricewaterhouseCoopers International)
Pyramid Analytics
Qlik
Quantum Corporation
Qubole
Rackspace
Radius Intelligence
RapidMiner
Recorded Future
Red Hat
Redis Labs
RedPoint Global
Reltio
Robert Bosch
Rocket Fuel
Rosenberger
RStudio
Ryft Systems
SAIC Motor Corporation
Sailthru
Salesforce.com
Salient Management Company
Samsung Group
SAP
SAS Institute
ScaleDB
ScaleOut Software
SCIO Health Analytics
Seagate Technology
Sinequa
SiSense
SnapLogic
Snowflake Computing
Software AG
Splice Machine
Splunk
Sqrrl
Strategy Companion Corporation
StreamSets
Striim
Subaru
Sumo Logic
Supermicro (Super Micro Computer)
Suzuki Motor Corporation
Syncsort
SynerScope
Tableau Software
Talena
Talend
Tamr
TARGIT
TCS (Tata Consultancy Services)
Teradata Corporation
Tesla
The Floow
ThoughtSpot
THTA (Tokyo Hire-Taxi Association)
TIBCO Software
Tidemark
TM Forum
Toshiba Corporation
Toyota Motor Corporation
TPC (Transaction Processing Performance Council)
Trifacta
Uber Technologies
Unravel Data
Valens
VMware
Volkswagen Group
VoltDB
Volvo Cars
W3C (World Wide Web Consortium)
Waterline Data
Western Digital Corporation
WiPro
Workday
Xevo
Xplenty
Yellowfin International
Yseop
Zendesk
Zoomdata
Zucchetti
 Table of Contents

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
		
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
		
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
		
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
		
Chapter 5: Automotive Industry Case Studies	70
5.1	Automotive OEMs	70
5.1.1	BMW: Eliminating Defects in New Vehicle Models with Big Data	70
5.1.2	Daimler: Ensuring Quality Assurance with Big Data	72
5.1.3	FCA (Fiat Chrysler Automobiles): Enhancing Dealer Management with Big Data	73
5.1.4	Ford Motor Company: Making Efficient Transportation Decisions with Big Data	74
5.1.5	GM (General Motors Company): Personalizing In-Vehicle Experience with Big Data	76
5.1.6	Groupe PSA: Reducing Industrial Energy Bills with Big Data	77
5.1.7	Groupe Renault: Boosting Driver Safety with Big Data	79
5.1.8	Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data	80
5.1.9	Hyundai Motor Company: Empowering Connected & Self-Driving Cars with Big Data	82
5.1.10	Jaguar Land Rover: Realizing Better & Cheaper Vehicle Designs with Big Data	83
5.1.11	Mazda Motor Corporation: Creating Better Engines with Big Data	84
5.1.12	Nissan Motor Company: Leveraging Big Data to Drive After-Sales Business Growth	85
5.1.13	SAIC Motor Corporation: Transforming Stressful Driving to Enjoyable Moments with Big Data	87
5.1.14	Subaru: Turbocharging Dealer Interaction with Big Data	88
5.1.15	Suzuki Motor Corporation: Accelerating Vehicle Design and Innovation with Big Data	89
5.1.16	Tesla: Achieving Customer Loyalty with Big Data	90
5.1.17	Toyota Motor Corporation: Powering Smart Cars with Big Data	91
5.1.18	Volkswagen Group: Transitioning to End-to-End Mobility Solutions with Big Data	93
5.1.19	Volvo Cars: Reducing Breakdowns and Failures with Big Data	95
5.2	Other Stakeholders	96
5.2.1	automotiveMastermind: Helping Automotive Dealerships Increase Sales with Big Data	96
5.2.2	Continental: Making Vehicles Safer with Big Data	97
5.2.3	Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data	98
5.2.4	Delphi Automotive: Monetizing Connected Vehicles with Big Data	99
5.2.5	Denso Corporation: Enabling Hazard Prediction with Big Data	100
5.2.6	HERE: Easing Traffic Congestion with Big Data	101
5.2.7	Lytx: Ensuring Road Safety with Big Data	102
5.2.8	Michelin: Optimizing Tire Manufacturing with Big Data	103
5.2.9	Robert Bosch: Empowering Fleet Management & Vehicle Insurance with Big Data	104
5.2.10	THTA (Tokyo Hire-Taxi Association): Making Connected Taxis a Reality with Big Data	105
5.2.11	Uber Technologies: Revolutionizing Ride Sourcing with Big Data	106
		
Chapter 6: Future Roadmap & Value Chain	108
6.1	Future Roadmap	108
6.1.1	2017 – 2020: Growing Investments in Real-Time & Predictive Analytics	108
6.1.2	2020 – 2025: Large-Scale Monetization of Automotive Big Data	109
6.1.3	2025 – 2030: Enabling Autonomous Driving & Future IoT Applications	109
6.2	Value Chain	110
6.2.1	Hardware Providers	110
6.2.1.1	Storage & Compute Infrastructure Providers	111
6.2.1.2	Networking Infrastructure Providers	111
6.2.2	Software Providers	112
6.2.2.1	Hadoop & Infrastructure Software Providers	112
6.2.2.2	SQL & NoSQL Providers	112
6.2.2.3	Analytic Platform & Application Software Providers	112
6.2.2.4	Cloud Platform Providers	113
6.2.3	Professional Services Providers	113
6.2.4	End-to-End Solution Providers	113
6.2.5	Automotive Industry	113
		
Chapter 7: Standardization & Regulatory Initiatives	114
7.1	ASF (Apache Software Foundation)	114
7.1.1	Management of Hadoop	114
7.1.2	Big Data Projects Beyond Hadoop	114
7.2	CSA (Cloud Security Alliance)	117
7.2.1	BDWG (Big Data Working Group)	117
7.3	CSCC (Cloud Standards Customer Council)	118
7.3.1	Big Data Working Group	118
7.4	DMG  (Data Mining Group)	119
7.4.1	PMML (Predictive Model Markup Language) Working Group	119
7.4.2	PFA (Portable Format for Analytics) Working Group	119
7.5	IEEE (Institute of Electrical and Electronics Engineers)	120
7.5.1	Big Data Initiative	120
7.6	INCITS (InterNational Committee for Information Technology Standards)	121
7.6.1	Big Data Technical Committee	121
7.7	ISO (International Organization for Standardization)	122
7.7.1	ISO/IEC JTC 1/SC 32: Data Management and Interchange	122
7.7.2	ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms	123
7.7.3	ISO/IEC JTC 1/SC 27: IT Security Techniques	123
7.7.4	ISO/IEC JTC 1/WG 9: Big Data	123
7.7.5	Collaborations with Other ISO Work Groups	125
7.8	ITU (International Telecommunications Union)	125
7.8.1	ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities	125
7.8.2	Other Deliverables Through SG (Study Group) 13 on Future Networks	126
7.8.3	Other Relevant Work	127
7.9	Linux Foundation	127
7.9.1	ODPi (Open Ecosystem of Big Data)	127
7.10	NIST (National Institute of Standards and Technology)	128
7.10.1	NBD-PWG (NIST Big Data Public Working Group)	128
7.11	OASIS (Organization for the Advancement of Structured Information Standards)	129
7.11.1	Technical Committees	129
7.12	ODaF (Open Data Foundation)	130
7.12.1	Big Data Accessibility	130
7.13	ODCA (Open Data Center Alliance)	130
7.13.1	Work on Big Data	130
7.14	OGC (Open Geospatial Consortium)	131
7.14.1	Big Data DWG (Domain Working Group)	131
7.15	TM Forum	131
7.15.1	Big Data Analytics Strategic Program	131
7.16	TPC (Transaction Processing Performance Council)	132
7.16.1	TPC-BDWG (TPC Big Data Working Group)	132
7.17	W3C (World Wide Web Consortium)	132
7.17.1	Big Data Community Group	132
7.17.2	Open Government Community Group	133
		
Chapter 8: Market Analysis & Forecasts	134
8.1	Global Outlook for Big Data in the Automotive Industry	134
8.2	Hardware, Software & Professional Services Segmentation	135
8.3	Horizontal Submarket Segmentation	136
8.4	Hardware Submarkets	136
8.4.1	Storage and Compute Infrastructure	136
8.4.2	Networking Infrastructure	137
8.5	Software Submarkets	137
8.5.1	Hadoop & Infrastructure Software	137
8.5.2	SQL	138
8.5.3	NoSQL	138
8.5.4	Analytic Platforms & Applications	139
8.5.5	Cloud Platforms	139
8.6	Professional Services Submarket	140
8.6.1	Professional Services	140
8.7	Application Area Segmentation	141
8.7.1	Product Development, Manufacturing & Supply Chain	141
8.7.2	After-Sales, Warranty & Dealer Management	142
8.7.3	Connected Vehicles & Intelligent Transportation	142
8.7.4	Marketing, Sales & Other Applications	143
8.8	Use Case Segmentation	144
8.9	Product Development, Manufacturing & Supply Chain Use Cases	145
8.9.1	Supply Chain Management	145
8.9.2	Manufacturing	145
8.9.3	Product Design & Planning	146
8.10	After-Sales, Warranty & Dealer Management Use Cases	146
8.10.1	Predictive Maintenance & Real-Time Diagnostics	146
8.10.2	Recall & Warranty Management	147
8.10.3	Parts Inventory & Pricing Optimization	147
8.10.4	Dealer Management & Customer Support Services	148
8.11	Connected Vehicles & Intelligent Transportation Use Cases	148
8.11.1	UBI (Usage-Based Insurance)	148
8.11.2	Autonomous & Semi-Autonomous Driving	149
8.11.3	Intelligent Transportation	149
8.11.4	Fleet Management	150
8.11.5	Driver Safety & Vehicle Cyber Security	150
8.11.6	In-Vehicle Experience, Navigation & Infotainment	151
8.11.7	Ride Sourcing, Sharing & Rentals	151
8.12	Marketing, Sales & Other Application Use Cases	152
8.12.1	Marketing & Sales	152
8.12.2	Customer Retention	152
8.12.3	Third Party Monetization	153
8.12.4	Other Use Cases	153
8.13	Regional Outlook	154
8.14	Asia Pacific	154
8.14.1	Country Level Segmentation	155
8.14.2	Australia	155
8.14.3	China	156
8.14.4	India	156
8.14.5	Indonesia	157
8.14.6	Japan	157
8.14.7	Malaysia	158
8.14.8	Pakistan	158
8.14.9	Philippines	159
8.14.10	Singapore	159
8.14.11	South Korea	160
8.14.12	Taiwan	160
8.14.13	Thailand	161
8.14.14	Rest of Asia Pacific	161
8.15	Eastern Europe	162
8.15.1	Country Level Segmentation	162
8.15.2	Czech Republic	163
8.15.3	Poland	163
8.15.4	Russia	164
8.15.5	Rest of Eastern Europe	164
8.16	Latin & Central America	165
8.16.1	Country Level Segmentation	165
8.16.2	Argentina	166
8.16.3	Brazil	166
8.16.4	Mexico	167
8.16.5	Rest of Latin & Central America	167
8.17	Middle East & Africa	168
8.17.1	Country Level Segmentation	168
8.17.2	Israel	169
8.17.3	Qatar	169
8.17.4	Saudi Arabia	170
8.17.5	South Africa	170
8.17.6	UAE	171
8.17.7	Rest of the Middle East & Africa	171
8.18	North America	172
8.18.1	Country Level Segmentation	172
8.18.2	Canada	173
8.18.3	USA	173
8.19	Western Europe	174
8.19.1	Country Level Segmentation	174
8.19.2	Denmark	175
8.19.3	Finland	175
8.19.4	France	176
8.19.5	Germany	176
8.19.6	Italy	177
8.19.7	Netherlands	177
8.19.8	Norway	178
8.19.9	Spain	178
8.19.10	Sweden	179
8.19.11	UK	179
8.19.12	Rest of Western Europe	180
		
Chapter 9: Vendor Landscape	181
9.1	1010data	181
9.2	Absolutdata	182
9.3	Accenture	183
9.4	Actian Corporation	184
9.5	Adaptive Insights	185
9.6	Advizor Solutions	186
9.7	AeroSpike	187
9.8	AFS Technologies	188
9.9	Alation	189
9.10	Algorithmia	190
9.11	Alluxio	191
9.12	Alpine Data	192
9.13	Alteryx	193
9.14	AMD (Advanced Micro Devices)	194
9.15	Apixio	195
9.16	Arcadia Data	196
9.17	Arimo	197
9.18	ARM	198
9.19	AtScale	199
9.20	Attivio	200
9.21	Attunity	201
9.22	Automated Insights	202
9.23	AWS (Amazon Web Services)	203
9.24	Axiomatics	204
9.25	Ayasdi	205
9.26	Basho Technologies	206
9.27	BCG (Boston Consulting Group)	207
9.28	Bedrock Data	208
9.29	BetterWorks	209
9.30	Big Cloud Analytics	210
9.31	BigML	211
9.32	Big Panda	212
9.33	Birst	213
9.34	Bitam	214
9.35	Blue Medora	215
9.36	BlueData Software	216
9.37	BlueTalon	217
9.38	BMC Software	218
9.39	BOARD International	219
9.40	Booz Allen Hamilton	220
9.41	Boxever	221
9.42	CACI International	222
9.43	Cambridge Semantics	223
9.44	Capgemini	224
9.45	Cazena	225
9.46	Centrifuge Systems	226
9.47	CenturyLink	227
9.48	Chartio	228
9.49	Cisco Systems	229
9.50	Civis Analytics	230
9.51	ClearStory Data	231
9.52	Cloudability	232
9.53	Cloudera	233
9.54	Clustrix	234
9.55	CognitiveScale	235
9.56	Collibra	236
9.57	Concurrent Computer Corporation	237
9.58	Confluent	238
9.59	Contexti	239
9.60	Continuum Analytics	240
9.61	Couchbase	241
9.62	CrowdFlower	242
9.63	Databricks	243
9.64	DataGravity	244
9.65	Dataiku	245
9.66	Datameer	246
9.67	DataRobot	247
9.68	DataScience	248
9.69	DataStax	249
9.70	DataTorrent	250
9.71	Datawatch Corporation	251
9.72	Datos IO	252
9.73	DDN (DataDirect Networks)	253
9.74	Decisyon	254
9.75	Dell Technologies	255
9.76	Deloitte	256
9.77	Demandbase	257
9.78	Denodo Technologies	258
9.79	Digital Reasoning Systems	259
9.80	Dimensional Insight	260
9.81	Dolphin Enterprise Solutions Corporation	261
9.82	Domino Data Lab	262
9.83	Domo	263
9.84	DriveScale	264
9.85	Dundas Data Visualization	265
9.86	DXC Technology	266
9.87	Eligotech	267
9.88	Engineering Group (Engineering Ingegneria Informatica)	268
9.89	EnterpriseDB	269
9.90	eQ Technologic	270
9.91	Ericsson	271
9.92	EXASOL	272
9.93	Facebook	273
9.94	FICO (Fair Isaac Corporation)	274
9.95	Fractal Analytics	275
9.96	Fujitsu	276
9.97	Fuzzy Logix	278
9.98	Gainsight	279
9.99	GE (General Electric)	280
9.100	Glassbeam	281
9.101	GoodData Corporation	282
9.102	Google	283
9.103	Greenwave Systems	284
9.104	GridGain Systems	285
9.105	Guavus	286
9.106	H2O.ai	287
9.107	HDS (Hitachi Data Systems)	288
9.108	Hedvig	289
9.109	Hortonworks	290
9.110	HPE (Hewlett Packard Enterprise)	291
9.111	Huawei	293
9.112	IBM Corporation	294
9.113	iDashboards	296
9.114	Impetus Technologies	297
9.115	Incorta	298
9.116	InetSoft Technology Corporation	299
9.117	Infer	300
9.118	Infor	301
9.119	Informatica Corporation	302
9.120	Information Builders	303
9.121	Infosys	304
9.122	Infoworks	305
9.123	Insightsoftware.com	306
9.124	InsightSquared	307
9.125	Intel Corporation	308
9.126	Interana	309
9.127	InterSystems Corporation	310
9.128	Jedox	311
9.129	Jethro	312
9.130	Jinfonet Software	313
9.131	Juniper Networks	314
9.132	KALEAO	315
9.133	Keen IO	316
9.134	Kinetica	317
9.135	KNIME	318
9.136	Kognitio	319
9.137	Kyvos Insights	320
9.138	Lavastorm	321
9.139	Lexalytics	322
9.140	Lexmark International	323
9.141	Logi Analytics	324
9.142	Longview Solutions	325
9.143	Looker Data Sciences	326
9.144	LucidWorks	327
9.145	Luminoso Technologies	328
9.146	Maana	329
9.147	Magento Commerce	330
9.148	Manthan Software Services	331
9.149	MapD Technologies	332
9.150	MapR Technologies	333
9.151	MariaDB Corporation	334
9.152	MarkLogic Corporation	335
9.153	Mathworks	336
9.154	MemSQL	337
9.155	Metric Insights	338
9.156	Microsoft Corporation	339
9.157	MicroStrategy	340
9.158	Minitab	341
9.159	MongoDB	342
9.160	Mu Sigma	343
9.161	NEC Corporation	344
9.162	Neo Technology	345
9.163	NetApp	346
9.164	Nimbix	347
9.165	Nokia	348
9.166	NTT Data Corporation	349
9.167	Numerify	350
9.168	NuoDB	351
9.169	Nutonian	352
9.170	NVIDIA Corporation	353
9.171	Oblong Industries	354
9.172	OpenText Corporation	355
9.173	Opera Solutions	357
9.174	Optimal Plus	358
9.175	Oracle Corporation	359
9.176	Palantir Technologies	361
9.177	Panorama Software	362
9.178	Paxata	363
9.179	Pentaho Corporation	364
9.180	Pepperdata	365
9.181	Phocas Software	366
9.182	Pivotal Software	367
9.183	Prognoz	369
9.184	Progress Software Corporation	370
9.185	PwC (PricewaterhouseCoopers International)	371
9.186	Pyramid Analytics	372
9.187	Qlik	373
9.188	Quantum Corporation	374
9.189	Qubole	375
9.190	Rackspace	376
9.191	Radius Intelligence	377
9.192	RapidMiner	378
9.193	Recorded Future	379
9.194	Red Hat	380
9.195	Redis Labs	381
9.196	RedPoint Global	382
9.197	Reltio	383
9.198	Rocket Fuel	384
9.199	RStudio	385
9.200	Ryft Systems	386
9.201	Sailthru	387
9.202	Salesforce.com	388
9.203	Salient Management Company	389
9.204	Samsung Group	390
9.205	SAP	391
9.206	SAS Institute	392
9.207	ScaleDB	393
9.208	ScaleOut Software	394
9.209	SCIO Health Analytics	395
9.210	Seagate Technology	396
9.211	Sinequa	397
9.212	SiSense	398
9.213	SnapLogic	399
9.214	Snowflake Computing	400
9.215	Software AG	401
9.216	Splice Machine	402
9.217	Splunk	403
9.218	Sqrrl	404
9.219	Strategy Companion Corporation	405
9.220	StreamSets	406
9.221	Striim	407
9.222	Sumo Logic	408
9.223	Supermicro (Super Micro Computer)	409
9.224	Syncsort	410
9.225	SynerScope	411
9.226	Tableau Software	412
9.227	Talena	413
9.228	Talend	414
9.229	Tamr	415
9.230	TARGIT	416
9.231	TCS (Tata Consultancy Services)	417
9.232	Teradata Corporation	418
9.233	ThoughtSpot	420
9.234	TIBCO Software	421
9.235	Tidemark	422
9.236	Toshiba Corporation	423
9.237	Trifacta	424
9.238	Unravel Data	425
9.239	VMware	426
9.240	VoltDB	427
9.241	Waterline Data	428
9.242	Western Digital Corporation	429
9.243	WiPro	430
9.244	Workday	431
9.245	Xplenty	432
9.246	Yellowfin International	433
9.247	Yseop	434
9.248	Zendesk	435
9.249	Zoomdata	436
9.250	Zucchetti	437
		
Chapter 10: Conclusion & Strategic Recommendations	438
10.1	Why is the Market Poised to Grow?	438
10.2	Geographic Outlook: Which Countries Offer the Highest Growth Potential?	438
10.3	Partnerships & M&A Activity: Highlighting the Importance of Big Data	439
10.4	Achieving Customer Retention with Data-Driven Services	440
10.5	Addressing Privacy Concerns	440
10.6	The Role of Legislation	441
10.7	Encouraging Data Sharing in the Automotive Industry	442
10.8	Assessing the Impact of Self-Driving Vehicles	442
10.9	Recommendations	443
10.9.1	Big Data Hardware, Software & Professional Services Providers	443
10.9.2	Automotive OEMS & Other Stakeholders	444
List of Figures

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



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