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自動車産業のビッグデータ 2017-2030年: ビジネスチャンス、課題、戦略、市場予測

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

 

出版社 出版年月電子媒体価格ページ数図表数
Signals and Systems Telecom
シグナルズアンドシステムズテレコム
2017年5月US$2,500
シングルユーザライセンス
445 96

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 2017-2030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。2017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、14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30のケーススタディ
  • ロードマップとバリューチェーン
  • 240社以上のビッグデータベンダの企業概要と戦略
  • 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
  • 2017-2030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 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.
 
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.
 
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
  • 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 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 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?
 
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
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

 



目次

1 Chapter 1: Introduction ........................... 22
1.1 Executive Summary ..................................... 22
1.2 Topics Covered .............................. 24
1.3 Forecast Segmentation ............................... 25
1.4 Key Questions Answered ............................. 28
1.5 Key Findings ................................... 29
1.6 Methodology ................................. 30
1.7 Target Audience ............................ 31
1.8 Companies & Organizations Mentioned ................... 32
2 Chapter 2: An Overview of Big Data ....................... 36
2.1 What is Big Data? .......................... 36
2.2 Key Approaches to Big Data Processing .................... 36
2.2.1 Hadoop .............................. 37
2.2.2 NoSQL ................................ 39
2.2.3 MPAD (Massively Parallel Analytic Databases) ......................... 39
2.2.4 In-Memory Processing ..................... 40
2.2.5 Stream Processing Technologies ................... 40
2.2.6 Spark .................................. 41
2.2.7 Other Databases & Analytic Technologies .................. 41
2.3 Key Characteristics of Big Data .................................. 42
2.3.1 Volume .............................. 42
2.3.2 Velocity .............................. 42
2.3.3 Variety ............................... 42
2.3.4 Value .................................. 43
2.4 Market Growth Drivers ............................... 44
2.4.1 Awareness of Benefits ..................... 44
2.4.2 Maturation of Big Data Platforms ............................... 44
2.4.3 Continued Investments by Web Giants, Governments & Enterprises .................. 45
2.4.4 Growth of Data Volume, Velocity & Variety ................ 45
2.4.5 Vendor Commitments & Partnerships ........................ 45
2.4.6 Technology Trends Lowering Entry Barriers ................ 46
2.5 Market Barriers ............................. 46
2.5.1 Lack of Analytic Specialists ............................ 46
2.5.2 Uncertain Big Data Strategies ........................ 46
2.5.3 Organizational Resistance to Big Data Adoption ....................... 47
2.5.4 Technical Challenges: Scalability & Maintenance ..................... 47
2.5.5 Security & Privacy Concerns .......................... 47
3 Chapter 3: Big Data Analytics ................... 49
3.1 What are Big Data Analytics? ................................. 49
3.2 The Importance of Analytics .................................. 49
3.3 Reactive vs. Proactive Analytics ............................. 50
3.4 Customer vs. Operational Analytics ....................... 51
3.5 Technology & Implementation Approaches ........................ 51
3.5.1 Grid Computing ............................... 51
3.5.2 In-Database Processing ................................. 52
3.5.3 In-Memory Analytics ....................... 52
3.5.4 Machine Learning & Data Mining .................. 52
3.5.5 Predictive Analytics ......................... 53
3.5.6 NLP (Natural Language Processing) ............................. 53
3.5.7 Text Analytics .................................. 54
3.5.8 Visual Analytics ................................ 55
3.5.9 Graph Analytics ............................... 55
3.5.10 Social Media, IT & Telco Network Analytics ................ 56
4 Chapter 4: Business Case & Applications in the Automotive Industry ............... 57
4.1 Overview & Investment Potential .......................... 57
4.2 Industry Specific Market Growth Drivers ............................. 58
4.3 Industry Specific Market Barriers ........................... 59
4.4 Key Applications ......................... 60
4.4.1 Product Development, Manufacturing & Supply Chain ............ 60
4.4.1.1 Optimizing the Supply Chain ....................... 60
4.4.1.2 Eliminating Manufacturing Defects ........................... 60
4.4.1.3 Customer-Driven Product Design & Planning ......................... 61
4.4.2 After-Sales, Warranty & Dealer Management .......................... 62
4.4.2.1 Predictive Maintenance & Real-Time Diagnostics .................. 62
4.4.2.2 Streamlining Recalls & Warranty .............................. 62
4.4.2.3 Parts Inventory & Pricing Optimization ..................... 63
4.4.2.4 Dealer Management & Customer Support Services ............... 63
4.4.3 Connected Vehicles & Intelligent Transportation ..................... 64
4.4.3.1 UBI (Usage-Based Insurance) ...................... 64
4.4.3.2 Autonomous & Semi-Autonomous Driving ............................. 64
4.4.3.3 Intelligent Transportation ........................... 65
4.4.3.4 Fleet Management ........................ 66
4.4.3.5 Driver Safety & Vehicle Cyber Security ..................... 66
4.4.3.6 In-Vehicle Experience, Navigation & Infotainment ................. 67
4.4.3.7 Ride Sourcing, Sharing & Rentals .............................. 67
4.4.4 Marketing, Sales & Other Applications ....................... 68
4.4.4.1 Marketing & Sales ......................... 68
4.4.4.2 Customer Retention .................................... 68
4.4.4.3 Third Party Monetization ............................ 68
4.4.4.4 Other Applications ........................ 69
5 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
6 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
7 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
8 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
9 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
10 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 .................................... 37
Figure 2: Reactive vs. Proactive Analytics .................................. 50
Figure 3: Distribution of Big Data Investments in the Automotive Industry, by Application Area: 2016 (%) ................. 57
Figure 4: Sensors in an Autonomous Vehicle ............................ 65
Figure 5: Toyota Smart Center Architecture .............................. 91
Figure 6: Big Data Roadmap in the Automotive Industry .................................... 108
Figure 7: Big Data Value Chain in the Automotive Industry ................................. 110
Figure 8: Key Aspects of Big Data Standardization ................................ 120
Figure 9: Global Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................ 134
Figure 10: Global Big Data Revenue in the Automotive Industry, by Hardware, Software & Professional Services: 2017 - 2030 ($ Million) ........ 135
Figure 11: Global Big Data Revenue in the Automotive Industry, by Submarket: 2017 - 2030 ($ Million)................... 136
Figure 12: Global Big Data Storage and Compute Infrastructure Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ........ 136
Figure 13: Global Big Data Networking Infrastructure Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .......... 137
Figure 14: Global Big Data Hadoop & Infrastructure Software Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ........... 137
Figure 15: Global Big Data SQL Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................. 138
Figure 16: Global Big Data NoSQL Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............. 138
Figure 17: Global Big Data Analytic Platforms & Applications Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............ 139
Figure 18: Global Big Data Cloud Platforms Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............ 139
Figure 19: Global Big Data Professional Services Submarket Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .................. 140
Figure 20: Global Big Data Revenue in the Automotive Industry, by Application Area: 2017 - 2030 ($ Million) ......... 141
Figure 21: Global Big Data Revenue in Automotive Product Development, Manufacturing & Supply Chain: 2017 - 2030 ($ Million) .... 141
Figure 22: Global Big Data Revenue in Automotive After-Sales, Warranty & Dealer Management: 2017 - 2030 ($ Million) .................. 142
Figure 23: Global Big Data Revenue in Connected Vehicles & Intelligent Transportation: 2017 - 2030 ($ Million) ................... 142
Figure 24: Global Big Data Revenue in Automotive Marketing, Sales & Other Applications: 2017 - 2030 ($ Million) ............... 143
Figure 25: Global Big Data Revenue in the Automotive Industry, by Use Case: 2017 - 2030 ($ Million) ..................... 144
Figure 26: Global Big Data Revenue in Automotive Supply Chain Management: 2017 - 2030 ($ Million) ................... 145
Figure 27: Global Big Data Revenue in Automotive Manufacturing: 2017 - 2030 ($ Million) ........................ 145
Figure 28: Global Big Data Revenue in Automotive Product Design & Planning: 2017 - 2030 ($ Million) ................... 146
Figure 29: Global Big Data Revenue in Automotive Predictive Maintenance & Real-Time Diagnostics: 2017 - 2030 ($ Million) ............. 146
Figure 30: Global Big Data Revenue in Automotive Recall & Warranty Management: 2017 - 2030 ($ Million)........... 147
Figure 31: Global Big Data Revenue in Automotive Parts Inventory & Pricing Optimization: 2017 - 2030 ($ Million) ............... 147
Figure 32: Global Big Data Revenue in Automotive Dealer Management & Customer Support Services: 2017 - 2030 ($ Million) .......... 148
Figure 33: Global Big Data Revenue in UBI (Usage-Based Insurance): 2017 - 2030 ($ Million) ...................... 148
Figure 34: Global Big Data Revenue in Autonomous & Semi-Autonomous Driving: 2017 - 2030 ($ Million) ............... 149
Figure 35: Global Big Data Revenue in Intelligent Transportation: 2017 - 2030 ($ Million) ........................... 149
Figure 36: Global Big Data Revenue in Fleet Management: 2017 - 2030 ($ Million) ....................... 150
Figure 37: Global Big Data Revenue in Driver Safety & Vehicle Cyber Security: 2017 - 2030 ($ Million) ..................... 150
Figure 38: Global Big Data Revenue in In-Vehicle Experience, Navigation & Infotainment: 2017 - 2030 ($ Million) ................. 151
Figure 39: Global Big Data Revenue in Ride Sourcing, Sharing & Rentals: 2017 - 2030 ($ Million) ................ 151
Figure 40: Global Big Data Revenue in Automotive Marketing & Sales: 2017 - 2030 ($ Million) ................... 152
Figure 41: Global Big Data Revenue in Automotive Customer Retention: 2017 - 2030 ($ Million) ................ 152
Figure 42: Global Big Data Revenue in Automotive Third Party Monetization: 2017 - 2030 ($ Million) ...................... 153
Figure 43: Global Big Data Revenue in Other Automotive Industry Use Cases: 2017 - 2030 ($ Million) ...................... 153
Figure 44: Big Data Revenue in the Automotive Industry, by Region: 2017 - 2030 ($ Million) ...................... 154
Figure 45: Asia Pacific Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ..................... 154
Figure 46: Asia Pacific Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) ................ 155
Figure 47: Australia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ......................... 155
Figure 48: China Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................ 156
Figure 49: India Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................. 156
Figure 50: Indonesia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ....................... 157
Figure 51: Japan Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................ 157
Figure 52: Malaysia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ......................... 158
Figure 53: Pakistan Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ......................... 158
Figure 54: Philippines Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ..................... 159
Figure 55: Singapore Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ....................... 159
Figure 56: South Korea Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................... 160
Figure 57: Taiwan Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ........................... 160
Figure 58: Thailand Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ......................... 161
Figure 59: Rest of Asia Pacific Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ....................... 161
Figure 60: Eastern Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .............. 162
Figure 61: Eastern Europe Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) ......... 162
Figure 62: Czech Republic Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............... 163
Figure 63: Poland Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............................ 163
Figure 64: Russia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............... 164
Figure 65: Rest of Eastern Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................ 164
Figure 66: Latin & Central America Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............... 165
Figure 67: Latin & Central America Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million).......... 165
Figure 68: Argentina Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ....................... 166
Figure 69: Brazil Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................ 166
Figure 70: Mexico Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ........................... 167
Figure 71: Rest of Latin & Central America Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................. 167
Figure 72: Middle East & Africa Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .................... 168
Figure 73: Middle East & Africa Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) ............... 168
Figure 74: Israel Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................ 169
Figure 75: Qatar Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................ 169
Figure 76: Saudi Arabia Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................... 170
Figure 77: South Africa Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................... 170
Figure 78: UAE Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .................. 171
Figure 79: Rest of the Middle East & Africa Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................ 171
Figure 80: North America Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............... 172
Figure 81: North America Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) .......... 172
Figure 82: Canada Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ........................... 173
Figure 83: USA Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .................. 173
Figure 84: Western Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............. 174
Figure 85: Western Europe Big Data Revenue in the Automotive Industry, by Country: 2017 - 2030 ($ Million) ..................... 174
Figure 86: Denmark Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ........................ 175
Figure 87: Finland Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ........................... 175
Figure 88: France Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............................ 176
Figure 89: Germany Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ........................ 176
Figure 90: Italy Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .................. 177
Figure 91: Netherlands Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................... 177
Figure 92: Norway Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .......................... 178
Figure 93: Spain Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ................ 178
Figure 94: Sweden Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .......................... 179
Figure 95: UK Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) .................... 179
Figure 96: Rest of Western Europe Big Data Revenue in the Automotive Industry: 2017 - 2030 ($ Million) ............... 180
 

 

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Big Data a $2.8 Billion opportunity in the automotive industry, says SNS Research report

 

SNS Research's latest report indicates that Big Data investments in the automotive industry are expected to surpass $2.8 Billion by the end of 2017.

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 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.

 

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