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自動車産業のビッグデータ 2018-2030年: ビジネスチャンス、課題、戦略、市場予測

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

 

出版社 出版年月ファイ ル/CD-ROM価格 ページ数図表数
SNS Telecom & IT
SNSテレコム&IT
2018年7月US$2,500
シングルユーザライセンス
501 99

サマリー

英国とドバイに拠点をおく調査会社シグナルズアンドシステムズテレコム/SNSリサーチ (Signals and Systems Telecom/SNS Research)の調査レポートの調査レポート「自動車産業のビッグデータ 2018-2030年: ビジネスチャンス、課題、戦略、市場予測」は、自動車産業におけるビッグデータの主要な市場促進要因や課題、投資の可能性、用途分野、利用事例、今後のロードマップ、バリューチェーン、ケーススタディ、ベンダの概要と戦略などを詳細に分析している。2018年から2030年のビッグデータのハードウェア、ソフトウェア、プロフェッショナルサービスの市場規模を、8つの水平サブ市場毎、4の用途分野毎、18の利用ケース毎、6つの地域と35ケ国毎に区分して予測している。

主なトピック

  • ビッグデータのエコシステム
  • 市場促進要因と阻害要因
  • 対応技術、標準、法規制のイニシアチブ
  • ビッグデータ解析と採用モデル
  • 自動車産業のビジネスケース、用途分野、利用ケース
  • 自動車OEMなどの利害関係者のビッグデータ投資の35以上のケーススタディ
  • ロードマップとバリューチェーン
  • 270社以上のビッグデータエコシステムにある主要企業と新興企業の概要と戦略
  • ビッグデータベンダ、自動車OEM、その他の利害関係者への戦略的助言
  • 2018-2030年の市場分析と予測
Synopsis: 
 
“Big Data” originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems.
 
Amid the proliferation of real-time and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The automotive industry is no exception to this trend, where Big Data has found a host of applications ranging from product design and manufacturing to predictive vehicle maintenance and autonomous driving. 
 
SNS Telecom & IT estimates that Big Data investments in the automotive industry will account for more than $3.3 Billion in 2018 alone. Led by a plethora of business opportunities for automotive OEMs, tier-1 suppliers, insurers, dealerships and other stakeholders, these investments are further expected to grow at a CAGR of approximately 16% over the next three years.
 
The “Big Data in the Automotive Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the automotive industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2018 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 4 application areas, 18 use cases, 6 regions and 35 countries.
 
The report comes with an associated Excel datasheet suite covering quantitative data from all numeric forecasts presented in the report.
 
Pricing: The report is available for the following price: 
 
Single User License: USD 2,500

Company Wide License: USD 3,500
 

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.
 
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 categories:
  • 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?
 
List of Companies Mentioned:

 

The following companies and organizations have been reviewed, discussed or mentioned in the report:
 

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

 



目次


1 Chapter 1: Introduction ....................... 24
1.1 Executive Summary ................................... 24
1.2 Topics Covered ..................................... 26
1.3 Forecast Segmentation ....................................... 27
1.4 Key Questions Answered ..................................... 30
1.5 Key Findings .......................................... 31
1.6 Methodology ........................................ 32
1.7 Target Audience ................................... 33
1.8 Companies & Organizations Mentioned ............................ 34

2 Chapter 2: An Overview of Big Data .................... 38
2.1 What is Big Data? ...................................... 38
2.2 Key Approaches to Big Data Processing ............................. 38
2.2.1 Hadoop ........................................... 39
2.2.2 NoSQL ........................................ 41
2.2.3 MPAD (Massively Parallel Analytic Databases) ............................ 41
2.2.4 In-Memory Processing ................................... 42
2.2.5 Stream Processing Technologies .............................. 42
2.2.6 Spark .......................................... 43
2.2.7 Other Databases & Analytic Technologies .............................. 43
2.3 Key Characteristics of Big Data ................................. 44
2.3.1 Volume ............................................ 44
2.3.2 Velocity ........................................... 44
2.3.3 Variety........................................ 44
2.3.4 Value .......................................... 45
2.4 Market Growth Drivers ....................................... 45
2.4.1 Awareness of Benefits ................................... 45
2.4.2 Maturation of Big Data Platforms ................................. 45
2.4.3 Continued Investments by Web Giants, Governments & Enterprises .................. 46
2.4.4 Growth of Data Volume, Velocity & Variety ........................... 46
2.4.5 Vendor Commitments & Partnerships ............................... 46
2.4.6 Technology Trends Lowering Entry Barriers ........................... 47
2.5 Market Barriers .................................... 47
2.5.1 Lack of Analytic Specialists ....................................... 47
2.5.2 Uncertain Big Data Strategies .................................. 47
2.5.3 Organizational Resistance to Big Data Adoption ......................... 48
2.5.4 Technical Challenges: Scalability & Maintenance ............................. 48
2.5.5 Security & Privacy Concerns .................................... 48

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 ................................ 50
3.5 Technology & Implementation Approaches ............................. 51
3.5.1 Grid Computing ......................................... 51
3.5.2 In-Database Processing ....................................... 51
3.5.3 In-Memory Analytics ...................................... 52
3.5.4 Machine Learning & Data Mining ................................. 52
3.5.5 Predictive Analytics ................................... 53
3.5.6 NLP (Natural Language Processing) .............................. 53
3.5.7 Text Analytics ............................................ 54
3.5.8 Visual Analytics ......................................... 54
3.5.9 Graph Analytics ......................................... 55
3.5.10 Social Media, IT & Telco Network Analytics ............................ 55

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 ............................. 61
4.4.2.1 Predictive Maintenance & Real-Time Diagnostics ............................ 61
4.4.2.2 Streamlining Recalls & Warranty .................................. 62
4.4.2.3 Parts Inventory & Pricing Optimization .............................. 62
4.4.2.4 Dealer Management & Customer Support Services ......................... 63
4.4.3 Connected Vehicles & Intelligent Transportation ............................. 63
4.4.3.1 UBI (Usage-Based Insurance) .............................. 63
4.4.3.2 Autonomous & Semi-Autonomous Driving ............................. 64
4.4.3.3 Intelligent Transportation ................................... 66
4.4.3.4 Fleet Management .................................... 66
4.4.3.5 Driver Safety & Vehicle Cyber Security .............................. 67
4.4.3.6 In-Vehicle Experience, Navigation & Infotainment ........................... 67
4.4.3.7 Ride Sourcing, Sharing & Rentals .................................. 67
4.4.4 Marketing, Sales & Other Applications .............................. 68
4.4.4.1 Marketing & Sales ..................................... 68
4.4.4.2 Customer Retention ....................................... 68
4.4.4.3 Third Party Monetization .................................... 69
4.4.4.4 Other Applications .................................... 69

5 Chapter 5: Automotive Industry Case Studies ............... 70
5.1 Automotive OEMs ....................................... 70
5.1.1 Audi: Facilitating Efficient Production Processes with Big Data ....................... 70
5.1.2 BMW: Eliminating Defects in New Vehicle Models with Big Data ........................ 72
5.1.3 Daimler: Ensuring Quality Assurance with Big Data .......................... 73
5.1.4 Dongfeng Motor Corporation: Enriching Network-Connected Autonomous Vehicles with Big Data ...... 74
5.1.5 FCA (Fiat Chrysler Automobiles): Enhancing Dealer Management with Big Data ................ 75
5.1.6 Ford Motor Company: Making Efficient Transportation Decisions with Big Data ............... 76
5.1.7 GM (General Motors Company): Personalizing In-Vehicle Experience with Big Data .......... 78
5.1.8 Groupe PSA: Reducing Industrial Energy Bills with Big Data ....................... 79
5.1.9 Groupe Renault: Boosting Driver Safety with Big Data .......................... 81
5.1.10 Honda Motor Company: Improving F1 Performance & Fuel Efficiency with Big Data .............. 82
5.1.11 Hyundai Motor Company: Empowering Connected & Self-Driving Cars with Big Data ............ 84
5.1.12 Jaguar Land Rover: Realizing Better & Cheaper Vehicle Designs with Big Data ................... 85
5.1.13 Mazda Motor Corporation: Creating Better Engines with Big Data ...................... 86
5.1.14 Nissan Motor Company: Leveraging Big Data to Drive After-Sales Business Growth .......... 87
5.1.15 SAIC Motor Corporation: Transforming Stressful Driving to Enjoyable Moments with Big Data ........ 89
5.1.16 Subaru: Turbocharging Dealer Interaction with Big Data ...................... 90
5.1.17 Suzuki Motor Corporation: Accelerating Vehicle Design and Innovation with Big Data ........... 91
5.1.18 Tesla: Achieving Customer Loyalty with Big Data ............................. 92
5.1.19 Toyota Motor Corporation: Powering Smart Cars with Big Data ..................... 93
5.1.20 Volkswagen Group: Transitioning to End-to-End Mobility Solutions with Big Data ............. 95
5.1.21 Volvo Cars: Reducing Breakdowns and Failures with Big Data ........................ 97
5.2 Other Stakeholders ..................................... 98
5.2.1 Allstate Corporation & Arity: Making Transportation Safer & Smarter with Big Data .............. 98
5.2.2 automotiveMastermind: Helping Automotive Dealerships Increase Sales with Big Data ........... 100
5.2.3 Continental: Making Vehicles Safer with Big Data .......................... 101
5.2.4 Cox Automotive: Transforming the Used Vehicle Lifecycle with Big Data ............... 102
5.2.5 Dash Labs: Turning Regular Cars into Data-Driven Smart Cars with Big Data ............... 103
5.2.6 Delphi Automotive: Monetizing Connected Vehicles with Big Data ................... 104
5.2.7 Denso Corporation: Enabling Hazard Prediction with Big Data ..................... 105
5.2.8 HERE: Easing Traffic Congestion with Big Data .......................... 106
5.2.9 Lytx: Ensuring Road Safety with Big Data.............................. 107
5.2.10 Michelin: Optimizing Tire Manufacturing with Big Data ...................... 108
5.2.11 Progressive Corporation: Rewarding Safe Drivers & Improving Traffic Safety with Big Data ........... 109
5.2.12 Bosch: Empowering Fleet Management & Vehicle Insurance with Big Data ................ 112
5.2.13 THTA (Tokyo Hire-Taxi Association): Making Connected Taxis a Reality with Big Data .......... 113
5.2.14 Uber Technologies: Revolutionizing Ride Sourcing with Big Data ....................... 114
5.2.15 U.S. Xpress: Driving Fuel-Savings with Big Data ......................... 115

6 Chapter 6: Future Roadmap & Value Chain ................. 117
6.1 Future Roadmap................................... 117
6.1.1 Pre-2020: Investments in Advanced Analytics for Vehicle-Related Services ................ 117
6.1.2 2020 – 2025: Proliferation of Real-Time Edge Analytics & Automotive Data Monetization ............. 118
6.1.3 2025 – 2030: Towards Fully Autonomous Driving & Future IoT Applications ............... 119
6.2 The Big Data Value Chain ............................... 120
6.2.1 Hardware Providers ..................................... 120
6.2.1.1 Storage & Compute Infrastructure Providers ............................ 120
6.2.1.2 Networking Infrastructure Providers ............................... 121
6.2.2 Software Providers ...................................... 121
6.2.2.1 Hadoop & Infrastructure Software Providers ............................ 122
6.2.2.2 SQL & NoSQL Providers ..................................... 122
6.2.2.3 Analytic Platform & Application Software Providers ...................... 122
6.2.2.4 Cloud Platform Providers .................................. 122
6.2.3 Professional Services Providers .................................. 123
6.2.4 End-to-End Solution Providers ............................... 123
6.2.5 Automotive Industry .................................... 123

7 Chapter 7: Standardization & Regulatory Initiatives .............. 124
7.1 ASF (Apache Software Foundation) .............................. 124
7.1.1 Management of Hadoop ................................... 124
7.1.2 Big Data Projects Beyond Hadoop .............................. 124
7.2 CSA (Cloud Security Alliance) .............................. 128
7.2.1 BDWG (Big Data Working Group) ............................... 128
7.3 CSCC (Cloud Standards Customer Council) ............................. 128
7.3.1 Big Data Working Group ................................... 129
7.4 DMG (Data Mining Group) ................................. 129
7.4.1 PMML (Predictive Model Markup Language) Working Group ....................... 130
7.4.2 PFA (Portable Format for Analytics) Working Group ........................... 130
7.5 IEEE (Institute of Electrical and Electronics Engineers) ..................... 130
7.5.1 Big Data Initiative .................................... 131
7.6 INCITS (InterNational Committee for Information Technology Standards) ............... 132
7.6.1 Big Data Technical Committee ............................... 132
7.7 ISO (International Organization for Standardization) ....................... 133
7.7.1 ISO/IEC JTC 1/SC 32: Data Management and Interchange ........................ 133
7.7.2 ISO/IEC JTC 1/SC 38: Cloud Computing and Distributed Platforms ..................... 134
7.7.3 ISO/IEC JTC 1/SC 27: IT Security Techniques ......................... 134
7.7.4 ISO/IEC JTC 1/WG 9: Big Data ................................ 134
7.7.5 Collaborations with Other ISO Work Groups ............................. 135
7.8 ITU (International Telecommunication Union) ............................ 136
7.8.1 ITU-T Y.3600: Big Data – Cloud Computing Based Requirements and Capabilities............ 136
7.8.2 Other Deliverables Through SG (Study Group) 13 on Future Networks .................. 137
7.8.3 Other Relevant Work ................................... 137
7.9 Linux Foundation ....................................... 138
7.9.1 ODPi (Open Ecosystem of Big Data) ................................. 138
7.10 NIST (National Institute of Standards and Technology) .................... 138
7.10.1 NBD-PWG (NIST Big Data Public Working Group) ........................... 138
7.11 OASIS (Organization for the Advancement of Structured Information Standards) ............. 139
7.11.1 Technical Committees....................................... 139
7.12 ODaF (Open Data Foundation) ................................. 140
7.12.1 Big Data Accessibility ................................... 140
7.13 ODCA (Open Data Center Alliance) ............................... 140
7.13.1 Work on Big Data .................................... 141
7.14 OGC (Open Geospatial Consortium) ............................. 141
7.14.1 Big Data DWG (Domain Working Group) .............................. 141
7.15 TM Forum ........................................ 141
7.15.1 Big Data Analytics Strategic Program ............................... 142
7.16 TPC (Transaction Processing Performance Council) ..................... 142
7.16.1 TPC-BDWG (TPC Big Data Working Group) ........................... 142
7.17 W3C (World Wide Web Consortium) ............................ 142
7.17.1 Big Data Community Group ................................... 143
7.17.2 Open Government Community Group.................................. 143

8 Chapter 8: Market Sizing & Forecasts ................ 144
8.1 Global Outlook for Big Data in the Automotive Industry ..................... 144
8.2 Hardware, Software & Professional Services Segmentation ..................... 145
8.3 Horizontal Submarket Segmentation ............................... 146
8.4 Hardware Submarkets ....................................... 146
8.4.1 Storage and Compute Infrastructure ............................... 146
8.4.2 Networking Infrastructure ..................................... 147
8.5 Software Submarkets ................................... 147
8.5.1 Hadoop & Infrastructure Software .................................. 147
8.5.2 SQL ........................................... 148
8.5.3 NoSQL ........................................... 148
8.5.4 Analytic Platforms & Applications............................... 149
8.5.5 Cloud Platforms ...................................... 149
8.6 Professional Services Submarket................................. 150
8.6.1 Professional Services ................................... 150
8.7 Application Area Segmentation .................................. 151
8.7.1 Product Development, Manufacturing & Supply Chain ....................... 151
8.7.2 After-Sales, Warranty & Dealer Management ........................... 152
8.7.3 Connected Vehicles & Intelligent Transportation ........................... 152
8.7.4 Marketing, Sales & Other Applications ................................. 153
8.8 Use Case Segmentation ..................................... 154
8.9 Product Development, Manufacturing & Supply Chain Use Cases ................ 155
8.9.1 Supply Chain Management .................................... 155
8.9.2 Manufacturing ........................................ 155
8.9.3 Product Design & Planning .................................... 156
8.10 After-Sales, Warranty & Dealer Management Use Cases ...................... 156
8.10.1 Predictive Maintenance & Real-Time Diagnostics .......................... 156
8.10.2 Recall & Warranty Management ................................ 157
8.10.3 Parts Inventory & Pricing Optimization ................................ 157
8.10.4 Dealer Management & Customer Support Services ............................ 158
8.11 Connected Vehicles & Intelligent Transportation Use Cases ...................... 158
8.11.1 UBI (Usage-Based Insurance) ................................. 158
8.11.2 Autonomous & Semi-Autonomous Driving ........................... 159
8.11.3 Intelligent Transportation ...................................... 159
8.11.4 Fleet Management ....................................... 160
8.11.5 Driver Safety & Vehicle Cyber Security ................................. 160
8.11.6 In-Vehicle Experience, Navigation & Infotainment ......................... 161
8.11.7 Ride Sourcing, Sharing & Rentals ................................ 161
8.12 Marketing, Sales & Other Application Use Cases ......................... 162
8.12.1 Marketing & Sales ................................... 162
8.12.2 Customer Retention ..................................... 162
8.12.3 Third Party Monetization ....................................... 163
8.12.4 Other Use Cases ...................................... 163
8.13 Regional Outlook ....................................... 164
8.14 Asia Pacific ....................................... 164
8.14.1 Country Level Segmentation .................................. 165
8.14.2 Australia ........................................ 165
8.14.3 China ........................................ 166
8.14.4 India ......................................... 166
8.14.5 Indonesia ........................................... 167
8.14.6 Japan ........................................ 167
8.14.7 Malaysia ........................................ 168
8.14.8 Pakistan......................................... 168
8.14.9 Philippines.......................................... 169
8.14.10 Singapore ........................................... 169
8.14.11 South Korea ............................................ 170
8.14.12 Taiwan .......................................... 170
8.14.13 Thailand ........................................ 171
8.14.14 Rest of Asia Pacific ....................................... 171
8.15 Eastern Europe ..................................... 172
8.15.1 Country Level Segmentation .................................. 172
8.15.2 Czech Republic ........................................ 173
8.15.3 Poland ........................................... 173
8.15.4 Russia ............................................ 174
8.15.5 Rest of Eastern Europe ..................................... 174
8.16 Latin & Central America ................................. 175
8.16.1 Country Level Segmentation .................................. 175
8.16.2 Argentina ........................................... 176
8.16.3 Brazil ........................................ 176
8.16.4 Mexico .......................................... 177
8.16.5 Rest of Latin & Central America .................................. 177
8.17 Middle East & Africa ....................................... 178
8.17.1 Country Level Segmentation .................................. 178
8.17.2 Israel ........................................ 179
8.17.3 Qatar ........................................ 179
8.17.4 Saudi Arabia ............................................ 180
8.17.5 South Africa ............................................ 180
8.17.6 UAE .......................................... 181
8.17.7 Rest of the Middle East & Africa ................................. 181
8.18 North America ...................................... 182
8.18.1 Country Level Segmentation .................................. 182
8.18.2 Canada .......................................... 183
8.18.3 USA .......................................... 183
8.19 Western Europe ................................... 184
8.19.1 Country Level Segmentation .................................. 184
8.19.2 Denmark ............................................ 185
8.19.3 Finland .......................................... 185
8.19.4 France ........................................... 186
8.19.5 Germany ............................................ 186
8.19.6 Italy .......................................... 187
8.19.7 Netherlands ............................................ 187
8.19.8 Norway ......................................... 188
8.19.9 Spain ........................................ 188
8.19.10 Sweden ......................................... 189
8.19.11 UK ............................................ 189
8.19.12 Rest of Western Europe .................................... 190

9 Chapter 9: Vendor Landscape ...................... 191
9.1 1010data ............................................ 191
9.2 Absolutdata ........................................ 192
9.3 Accenture ........................................... 193
9.4 Actian Corporation/HCL Technologies ............................. 194
9.5 Adaptive Insights ..................................... 196
9.6 Adobe Systems ................................... 197
9.7 Advizor Solutions ..................................... 199
9.8 AeroSpike ........................................... 200
9.9 AFS Technologies ..................................... 201
9.10 Alation ........................................ 202
9.11 Algorithmia ...................................... 203
9.12 Alluxio ......................................... 204
9.13 ALTEN ......................................... 205
9.14 Alteryx ........................................ 206
9.15 AMD (Advanced Micro Devices) ............................... 207
9.16 Anaconda ........................................ 208
9.17 Apixio ......................................... 209
9.18 Arcadia Data .................................... 210
9.19 ARM ............................................ 211
9.20 AtScale ........................................ 212
9.21 Attivio ......................................... 213
9.22 Attunity ........................................... 214
9.23 Automated Insights ................................... 215
9.24 AVORA ........................................ 216
9.25 AWS (Amazon Web Services) .............................. 217
9.26 Axiomatics ....................................... 219
9.27 Ayasdi ......................................... 220
9.28 BackOffice Associates ..................................... 221
9.29 Basho Technologies ................................... 222
9.30 BCG (Boston Consulting Group) ............................... 223
9.31 Bedrock Data ................................... 224
9.32 BetterWorks .................................... 225
9.33 Big Panda ......................................... 226
9.34 BigML ......................................... 227
9.35 Bitam .......................................... 228
9.36 Blue Medora .................................... 229
9.37 BlueData Software .................................... 230
9.38 BlueTalon ........................................ 231
9.39 BMC Software ...................................... 232
9.40 BOARD International ...................................... 233
9.41 Booz Allen Hamilton ....................................... 234
9.42 Boxever ........................................... 235
9.43 CACI International ..................................... 236
9.44 Cambridge Semantics ..................................... 237
9.45 Capgemini ....................................... 238
9.46 Cazena ........................................ 239
9.47 Centrifuge Systems ................................... 240
9.48 CenturyLink ..................................... 241
9.49 Chartio ........................................ 242
9.50 Cisco Systems ....................................... 243
9.51 Civis Analytics ....................................... 244
9.52 ClearStory Data .................................... 245
9.53 Cloudability ..................................... 246
9.54 Cloudera .......................................... 247
9.55 Cloudian .......................................... 248
9.56 Clustrix ............................................ 249
9.57 CognitiveScale ...................................... 250
9.58 Collibra ............................................ 251
9.59 Concurrent Technology/Vecima Networks ............................. 252
9.60 Confluent ......................................... 253
9.61 Contexti ........................................... 254
9.62 Couchbase ....................................... 255
9.63 Crate.io ............................................ 256
9.64 Cray ............................................ 257
9.65 Databricks ....................................... 258
9.66 Dataiku ............................................ 259
9.67 Datalytyx ......................................... 260
9.68 Datameer ........................................ 261
9.69 DataRobot ....................................... 262
9.70 DataStax .......................................... 263
9.71 Datawatch Corporation .................................. 264
9.72 DDN (DataDirect Networks) ................................ 265
9.73 Decisyon .......................................... 266
9.74 Dell Technologies ...................................... 267
9.75 Deloitte............................................ 268
9.76 Demandbase ................................... 269
9.77 Denodo Technologies ..................................... 270
9.78 Dianomic Systems ..................................... 271
9.79 Digital Reasoning Systems .............................. 272
9.80 Dimensional Insight ................................... 273
9.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group ................... 274
9.82 Domino Data Lab ....................................... 275
9.83 Domo .......................................... 276
9.84 Dremio........................................ 277
9.85 DriveScale ........................................ 278
9.86 Druva .......................................... 279
9.87 Dundas Data Visualization .............................. 280
9.88 DXC Technology ................................... 281
9.89 Elastic ......................................... 282
9.90 Engineering Group (Engineering Ingegneria Informatica) ..................... 283
9.91 EnterpriseDB Corporation .............................. 284
9.92 eQ Technologic ..................................... 285
9.93 Ericsson ........................................... 286
9.94 Erwin .......................................... 287
9.95 EVŌ (Big Cloud Analytics) ............................... 288
9.96 EXASOL ............................................ 289
9.97 EXL (ExlService Holdings)................................ 290
9.98 Facebook ......................................... 291
9.99 FICO (Fair Isaac Corporation) .............................. 292
9.100 Figure Eight ...................................... 293
9.101 FogHorn Systems ....................................... 294
9.102 Fractal Analytics .................................... 295
9.103 Franz ........................................... 296
9.104 Fujitsu ......................................... 297
9.105 Fuzzy Logix ....................................... 299
9.106 Gainsight ..................................... 300
9.107 GE (General Electric) .................................. 301
9.108 Glassbeam ................................... 302
9.109 GoodData Corporation .............................. 303
9.110 Google/Alphabet................................... 304
9.111 Grakn Labs ....................................... 306
9.112 Greenwave Systems ................................... 307
9.113 GridGain Systems ....................................... 308
9.114 H2O.ai ......................................... 309
9.115 HarperDB .................................... 310
9.116 Hedvig ......................................... 311
9.117 Hitachi Vantara ..................................... 312
9.118 Hortonworks .................................... 313
9.119 HPE (Hewlett Packard Enterprise) ............................ 314
9.120 Huawei ........................................ 316
9.121 HVR ........................................ 317
9.122 HyperScience ................................... 318
9.123 HyTrust ........................................ 319
9.124 IBM Corporation ................................... 321
9.125 iDashboards ..................................... 323
9.126 IDERA .......................................... 324
9.127 Ignite Technologies .................................... 325
9.128 Imanis Data ...................................... 327
9.129 Impetus Technologies ................................ 328
9.130 Incorta ......................................... 329
9.131 InetSoft Technology Corporation.............................. 330
9.132 InfluxData .................................... 331
9.133 Infogix ......................................... 332
9.134 Infor/Birst.................................... 333
9.135 Informatica ...................................... 335
9.136 Information Builders .................................. 336
9.137 Infosys ......................................... 337
9.138 Infoworks .................................... 338
9.139 Insightsoftware.com .................................. 339
9.140 InsightSquared ...................................... 340
9.141 Intel Corporation ....................................... 341
9.142 Interana ...................................... 342
9.143 InterSystems Corporation ............................... 343
9.144 Jedox ........................................... 344
9.145 Jethro .......................................... 345
9.146 Jinfonet Software ....................................... 346
9.147 Juniper Networks ....................................... 347
9.148 KALEAO ....................................... 348
9.149 Keen IO........................................ 349
9.150 Keyrus ......................................... 350
9.151 Kinetica ....................................... 351
9.152 KNIME ......................................... 352
9.153 Kognitio ....................................... 353
9.154 Kyvos Insights ................................... 354
9.155 LeanXcale .................................... 355
9.156 Lexalytics ..................................... 356
9.157 Lexmark International ................................ 358
9.158 Lightbend .................................... 359
9.159 Logi Analytics ................................... 360
9.160 Logical Clocks ................................... 361
9.161 Longview Solutions/Tidemark .................................. 362
9.162 Looker Data Sciences ................................. 364
9.163 LucidWorks ...................................... 365
9.164 Luminoso Technologies .................................. 366
9.165 Maana ......................................... 367
9.166 Manthan Software Services ................................. 368
9.167 MapD Technologies ................................... 369
9.168 MapR Technologies .................................... 370
9.169 MariaDB Corporation ................................. 371
9.170 MarkLogic Corporation .............................. 372
9.171 Mathworks ....................................... 373
9.172 Melissa ........................................ 374
9.173 MemSQL ..................................... 375
9.174 Metric Insights ...................................... 376
9.175 Microsoft Corporation ............................... 377
9.176 MicroStrategy ....................................... 379
9.177 Minitab........................................ 380
9.178 MongoDB .................................... 381
9.179 Mu Sigma .................................... 382
9.180 NEC Corporation ................................... 383
9.181 Neo4j ........................................... 384
9.182 NetApp ........................................ 385
9.183 Nimbix ......................................... 386
9.184 Nokia ........................................... 387
9.185 NTT Data Corporation ................................ 388
9.186 Numerify ..................................... 389
9.187 NuoDB ......................................... 390
9.188 NVIDIA Corporation ................................... 391
9.189 Objectivity ................................... 392
9.190 Oblong Industries ....................................... 393
9.191 OpenText Corporation ............................... 394
9.192 Opera Solutions .................................... 396
9.193 Optimal Plus ..................................... 397
9.194 Oracle Corporation .................................... 398
9.195 Palantir Technologies ................................. 401
9.196 Panasonic Corporation/Arimo .................................. 403
9.197 Panorama Software ................................... 404
9.198 Paxata ......................................... 405
9.199 Pepperdata ...................................... 406
9.200 Phocas Software ................................... 407
9.201 Pivotal Software .................................... 408
9.202 Prognoz ....................................... 410
9.203 Progress Software Corporation ................................ 411
9.204 Provalis Research ....................................... 412
9.205 Pure Storage .................................... 413
9.206 PwC (PricewaterhouseCoopers International) ........................ 414
9.207 Pyramid Analytics ...................................... 415
9.208 Qlik ......................................... 416
9.209 Qrama/Tengu ................................... 417
9.210 Quantum Corporation ............................... 418
9.211 Qubole ........................................ 419
9.212 Rackspace ................................... 420
9.213 Radius Intelligence ..................................... 421
9.214 RapidMiner ...................................... 422
9.215 Recorded Future ................................... 423
9.216 Red Hat ....................................... 424
9.217 Redis Labs ................................... 425
9.218 RedPoint Global .................................... 426
9.219 Reltio ........................................... 427
9.220 RStudio ........................................ 428
9.221 Rubrik/Datos IO .................................... 429
9.222 Ryft ......................................... 430
9.223 Sailthru ........................................ 431
9.224 Salesforce.com ...................................... 432
9.225 Salient Management Company ................................ 433
9.226 Samsung Group ..................................... 434
9.227 SAP ......................................... 435
9.228 SAS Institute ..................................... 436
9.229 ScaleOut Software ..................................... 437
9.230 Seagate Technology ................................... 438
9.231 Sinequa ....................................... 439
9.232 SiSense ........................................ 440
9.233 Sizmek ......................................... 441
9.234 SnapLogic .................................... 442
9.235 Snowflake Computing ................................ 443
9.236 Software AG ..................................... 444
9.237 Splice Machine ...................................... 445
9.238 Splunk ......................................... 446
9.239 Strategy Companion Corporation ............................. 448
9.240 Stratio ......................................... 449
9.241 Streamlio ..................................... 450
9.242 StreamSets ....................................... 451
9.243 Striim ........................................... 452
9.244 Sumo Logic ....................................... 453
9.245 Supermicro (Super Micro Computer) ............................ 454
9.246 Syncsort ...................................... 455
9.247 SynerScope ...................................... 457
9.248 SYNTASA ..................................... 458
9.249 Tableau Software ....................................... 459
9.250 Talend ......................................... 460
9.251 Tamr ............................................ 461
9.252 TARGIT ........................................ 462
9.253 TCS (Tata Consultancy Services) ............................... 463
9.254 Teradata Corporation ................................ 464
9.255 Thales/Guavus ...................................... 466
9.256 ThoughtSpot .................................... 467
9.257 TIBCO Software ..................................... 468
9.258 Toshiba Corporation .................................. 470
9.259 Transwarp ................................... 471
9.260 Trifacta ........................................ 472
9.261 Unifi Software ....................................... 473
9.262 Unravel Data .................................... 474
9.263 VANTIQ ....................................... 475
9.264 VMware ...................................... 476
9.265 VoltDB ......................................... 477
9.266 WANdisco ................................... 478
9.267 Waterline Data ...................................... 479
9.268 Western Digital Corporation ................................ 480
9.269 WhereScape ..................................... 481
9.270 WiPro .......................................... 482
9.271 Wolfram Research ..................................... 483
9.272 Workday ...................................... 485
9.273 Xplenty ........................................ 487
9.274 Yellowfin BI ...................................... 488
9.275 Yseop........................................... 489
9.276 Zendesk ....................................... 490
9.277 Zoomdata .................................... 491
9.278 Zucchetti ..................................... 492

10 Chapter 10: Conclusion & Strategic Recommendations .............. 493
10.1 Why is the Market Poised to Grow? ............................. 493
10.2 Geographic Outlook: Which Countries Offer the Highest Growth Potential? ........... 493
10.3 Partnerships & M&A Activity: Highlighting the Importance of Big Data ............... 494
10.4 The Significance of Edge Analytics for Automotive Applications ................ 495
10.5 Achieving Customer Retention with Data-Driven Services .................... 496
10.6 Addressing Privacy Concerns .............................. 496
10.7 The Role of Legislation ................................... 497
10.8 Encouraging Data Sharing in the Automotive Industry ..................... 498
10.9 Assessing the Impact of Self-Driving Vehicles ......................... 498
10.10 Recommendations ..................................... 499
10.10.1 Big Data Hardware, Software & Professional Services Providers........................ 499
10.10.2 Automotive OEMS & Other Stakeholders ............................. 500


List of Figures

Figure 1: Hadoop Architecture .............................................. 39
Figure 2: Reactive vs. Proactive Analytics ........................................ 50
Figure 3: Distribution of Big Data Investments in the Automotive Industry, by Application Area: 2018 (%) ................... 57
Figure 4: Autonomous Vehicle Generated Data Volume by Sensor (%) .................................. 64
Figure 5: On-Board Sensors in an Autonomous Vehicle ...................................... 65
Figure 6: Audi's Enterprise Big Data Platform ............................................ 71
Figure 7: Toyota's Smart Center Architecture ........................................... 93
Figure 8: Progressive Corporation's Use of Big Data for Automotive Insurance ......................... 110
Figure 9: Big Data Roadmap in the Automotive Industry: 2018 – 2030 ................................. 117
Figure 10: Big Data Value Chain in the Automotive Industry ....................................... 120
Figure 11: Key Aspects of Big Data Standardization ..................................... 131
Figure 12: Global Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................ 144
Figure 13: Global Big Data Revenue in the Automotive Industry, by Hardware, Software & Professional Services: 2018 – 2030 ($ Million) ....... 145
Figure 14: Global Big Data Revenue in the Automotive Industry, by Submarket: 2018 – 2030 ($ Million) ............... 146
Figure 15: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ....... 146
Figure 16: Global Big Data Networking Infrastructure Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........ 147
Figure 17: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ..... 147
Figure 18: Global Big Data SQL Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million)................... 148
Figure 19: Global Big Data NoSQL Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ................... 148
Figure 20: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ...... 149
Figure 21: Global Big Data Cloud Platforms Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) .............. 149
Figure 22: Global Big Data Professional Services Submarket Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........... 150
Figure 23: Global Big Data Revenue in the Automotive Industry, by Application Area: 2018 – 2030 ($ Million) ................ 151
Figure 24: Global Big Data Revenue in Automotive Product Development, Manufacturing & Supply Chain: 2018 – 2030 ($ Million)........ 151
Figure 25: Global Big Data Revenue in Automotive After-Sales, Warranty & Dealer Management: 2018 – 2030 ($ Million) ........... 152
Figure 26: Global Big Data Revenue in Connected Vehicles & Intelligent Transportation: 2018 – 2030 ($ Million) ................ 152
Figure 27: Global Big Data Revenue in Automotive Marketing, Sales & Other Applications: 2018 – 2030 ($ Million) ............ 153
Figure 28: Global Big Data Revenue in the Automotive Industry, by Use Case: 2018 – 2030 ($ Million) .................. 154
Figure 29: Global Big Data Revenue in Automotive Supply Chain Management: 2018 – 2030 ($ Million) ............... 155
Figure 30: Global Big Data Revenue in Automotive Manufacturing: 2018 – 2030 ($ Million) ........................ 155
Figure 31: Global Big Data Revenue in Automotive Product Design & Planning: 2018 – 2030 ($ Million) ................ 156
Figure 32: Global Big Data Revenue in Automotive Predictive Maintenance & Real-Time Diagnostics: 2018 – 2030 ($ Million) ........... 156
Figure 33: Global Big Data Revenue in Automotive Recall & Warranty Management: 2018 – 2030 ($ Million) ................. 157
Figure 34: Global Big Data Revenue in Automotive Parts Inventory & Pricing Optimization: 2018 – 2030 ($ Million) ............ 157
Figure 35: Global Big Data Revenue in Automotive Dealer Management & Customer Support Services: 2018 – 2030 ($ Million) ........ 158
Figure 36: Global Big Data Revenue in UBI (Usage-Based Insurance): 2018 – 2030 ($ Million) ...................... 158
Figure 37: Global Big Data Revenue in Autonomous & Semi-Autonomous Driving: 2018 – 2030 ($ Million) ................ 159
Figure 38: Global Big Data Revenue in Intelligent Transportation: 2018 – 2030 ($ Million) ...................... 159
Figure 39: Global Big Data Revenue in Fleet Management: 2018 – 2030 ($ Million)............................ 160
Figure 40: Global Big Data Revenue in Driver Safety & Vehicle Cyber Security: 2018 – 2030 ($ Million) ................. 160
Figure 41: Global Big Data Revenue in In-Vehicle Experience, Navigation & Infotainment: 2018 – 2030 ($ Million) .............. 161
Figure 42: Global Big Data Revenue in Ride Sourcing, Sharing & Rentals: 2018 – 2030 ($ Million) ..................... 161
Figure 43: Global Big Data Revenue in Automotive Marketing & Sales: 2018 – 2030 ($ Million) ........................ 162
Figure 44: Global Big Data Revenue in Automotive Customer Retention: 2018 – 2030 ($ Million) ..................... 162
Figure 45: Global Big Data Revenue in Automotive Third Party Monetization: 2018 – 2030 ($ Million) .................. 163
Figure 46: Global Big Data Revenue in Other Automotive Industry Use Cases: 2018 – 2030 ($ Million) .................. 163
Figure 47: Big Data Revenue in the Automotive Industry, by Region: 2018 – 2030 ($ Million) ...................... 164
Figure 48: Asia Pacific Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ..................... 164
Figure 49: Asia Pacific Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) ................. 165
Figure 50: Australia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) .................... 165
Figure 51: China Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ......................... 166
Figure 52: India Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) .......................... 166
Figure 53: Indonesia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ....................... 167
Figure 54: Japan Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ......................... 167
Figure 55: Malaysia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) .................... 168
Figure 56: Pakistan Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ..................... 168
Figure 57: Philippines Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ...................... 169
Figure 58: Singapore Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ....................... 169
Figure 59: South Korea Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................ 170
Figure 60: Taiwan Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ....................... 170
Figure 61: Thailand Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) .................... 171
Figure 62: Rest of Asia Pacific Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ................... 171
Figure 63: Eastern Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) .................... 172
Figure 64: Eastern Europe Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) ............... 172
Figure 65: Czech Republic Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) .................... 173
Figure 66: Poland Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ....................... 173
Figure 67: Russia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................ 174
Figure 68: Rest of Eastern Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) .................. 174
Figure 69: Latin & Central America Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ................ 175
Figure 70: Latin & Central America Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) ............ 175
Figure 71: Argentina Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ....................... 176
Figure 72: Brazil Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ......................... 176
Figure 73: Mexico Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ...................... 177
Figure 74: Rest of Latin & Central America Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) .............. 177
Figure 75: Middle East & Africa Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million)................. 178
Figure 76: Middle East & Africa Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) ............ 178
Figure 77: Israel Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ......................... 179
Figure 78: Qatar Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ......................... 179
Figure 79: Saudi Arabia Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................ 180
Figure 80: South Africa Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million).................... 180
Figure 81: UAE Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................... 181
Figure 82: Rest of the Middle East & Africa Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ............. 181
Figure 83: North America Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ..................... 182
Figure 84: North America Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) ................ 182
Figure 85: Canada Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ...................... 183
Figure 86: USA Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................... 183
Figure 87: Western Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ....................... 184
Figure 88: Western Europe Big Data Revenue in the Automotive Industry, by Country: 2018 – 2030 ($ Million) ................... 184
Figure 89: Denmark Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................ 185
Figure 90: Finland Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ...................... 185
Figure 91: France Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ....................... 186
Figure 92: Germany Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................ 186
Figure 93: Italy Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................... 187
Figure 94: Netherlands Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ........................ 187
Figure 95: Norway Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ...................... 188
Figure 96: Spain Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ......................... 188
Figure 97: Sweden Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ..................... 189
Figure 98: UK Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ............................. 189
Figure 99: Rest of Western Europe Big Data Revenue in the Automotive Industry: 2018 – 2030 ($ Million) ................ 190
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Big Data a $3.3 Billion opportunity in the automotive industry, says SNS Telecom & IT

2018/07/15

SNS Telecom & IT's latest report indicates that Big Data investments in the automotive industry are expected to surpass $3.3 Billion by the end of 2018.

Originally used as a term to describe datasets whose size is beyond the ability of traditional databases, the scope of Big Data has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data, to solve complex problems.

Amid the proliferation of real-time and historical data from sources such as connected devices, web, social media, sensors, log files and transactional applications, Big Data is rapidly gaining traction from a diverse range of vertical sectors. The automotive industry is no exception to this trend, where Big Data has found a host of applications ranging from product design and manufacturing to predictive vehicle maintenance and autonomous driving.

SNS Telecom & IT estimates that Big Data investments in the automotive industry will account for more than $3.3 Billion in 2018 alone. Led by a plethora of business opportunities for automotive OEMs, tier-1 suppliers, insurers, dealerships and other stakeholders, these investments are further expected to grow at a CAGR of approximately 16% over the next three years.

The “Big Data in the Automotive Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts” report presents an in-depth assessment of Big Data in the automotive industry including key market drivers, challenges, investment potential, application areas, use cases, future roadmap, value chain, case studies, vendor profiles and strategies. The report also presents market size forecasts for Big Data hardware, software and professional services investments from 2018 through to 2030. The forecasts are segmented for 8 horizontal submarkets, 4 application areas, 18 use cases, 6 regions and 35 countries.

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