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

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

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

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.

 

1 Chapter 1: Introduction
1.1 Executive Summary
1.2 Topics Covered
1.3 Forecast Segmentation
1.4 Key Questions Answered
1.5 Key Findings
1.6 Methodology
1.7 Target Audience
1.8 Companies & Organizations Mentioned

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

3 Chapter 3: Big Data Analytics
3.1 What are Big Data Analytics?
3.2 The Importance of Analytics
3.3 Reactive vs. Proactive Analytics
3.4 Customer vs. Operational Analytics
3.5 Technology & Implementation Approaches
3.5.1 Grid Computing
3.5.2 In-Database Processing
3.5.3 In-Memory Analytics
3.5.4 Machine Learning & Data Mining
3.5.5 Predictive Analytics
3.5.6 NLP (Natural Language Processing)
3.5.7 Text Analytics
3.5.8 Visual Analytics
3.5.9 Graph Analytics
3.5.10 Social Media, IT & Telco Network Analytics

4 Chapter 4: Business Case & Applications in the Automotive Industry
4.1 Overview & Investment Potential
4.2 Industry Specific Market Growth Drivers
4.3 Industry Specific Market Barriers
4.4 Key Applications
4.4.1 Product Development, Manufacturing & Supply Chain
4.4.1.1 Optimizing the Supply Chain
4.4.1.2 Eliminating Manufacturing Defects
4.4.1.3 Customer-Driven Product Design & Planning
4.4.2 After-Sales, Warranty & Dealer Management
4.4.2.1 Predictive Maintenance & Real-Time Diagnostics
4.4.2.2 Streamlining Recalls & Warranty
4.4.2.3 Parts Inventory & Pricing Optimization
4.4.2.4 Dealer Management & Customer Support Services
4.4.3 Connected Vehicles & Intelligent Transportation
4.4.3.1 UBI (Usage-Based Insurance)
4.4.3.2 Autonomous & Semi-Autonomous Driving
4.4.3.3 Intelligent Transportation
4.4.3.4 Fleet Management
4.4.3.5 Driver Safety & Vehicle Cyber Security
4.4.3.6 In-Vehicle Experience, Navigation & Infotainment
4.4.3.7 Ride Sourcing, Sharing & Rentals
4.4.4 Marketing, Sales & Other Applications
4.4.4.1 Marketing & Sales
4.4.4.2 Customer Retention
4.4.4.3 Third Party Monetization
4.4.4.4 Other Applications

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

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

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

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

9 Chapter 9: Vendor Landscape
9.1 1010data
9.2 Absolutdata
9.3 Accenture
9.4 Actian Corporation/HCL Technologies
9.5 Adaptive Insights
9.6 Adobe Systems
9.7 Advizor Solutions
9.8 AeroSpike
9.9 AFS Technologies
9.10 Alation
9.11 Algorithmia
9.12 Alluxio
9.13 ALTEN
9.14 Alteryx
9.15 AMD (Advanced Micro Devices)
9.16 Anaconda
9.17 Apixio
9.18 Arcadia Data
9.19 ARM
9.20 AtScale
9.21 Attivio
9.22 Attunity
9.23 Automated Insights
9.24 AVORA
9.25 AWS (Amazon Web Services)
9.26 Axiomatics
9.27 Ayasdi
9.28 BackOffice Associates
9.29 Basho Technologies
9.30 BCG (Boston Consulting Group)
9.31 Bedrock Data
9.32 BetterWorks
9.33 Big Panda
9.34 BigML
9.35 Bitam
9.36 Blue Medora
9.37 BlueData Software
9.38 BlueTalon
9.39 BMC Software
9.40 BOARD International
9.41 Booz Allen Hamilton
9.42 Boxever
9.43 CACI International
9.44 Cambridge Semantics
9.45 Capgemini
9.46 Cazena
9.47 Centrifuge Systems
9.48 CenturyLink
9.49 Chartio
9.50 Cisco Systems
9.51 Civis Analytics
9.52 ClearStory Data
9.53 Cloudability
9.54 Cloudera
9.55 Cloudian
9.56 Clustrix
9.57 CognitiveScale
9.58 Collibra
9.59 Concurrent Technology/Vecima Networks
9.60 Confluent
9.61 Contexti
9.62 Couchbase
9.63 Crate.io
9.64 Cray
9.65 Databricks
9.66 Dataiku
9.67 Datalytyx
9.68 Datameer
9.69 DataRobot
9.70 DataStax
9.71 Datawatch Corporation
9.72 DDN (DataDirect Networks)
9.73 Decisyon
9.74 Dell Technologies
9.75 Deloitte
9.76 Demandbase
9.77 Denodo Technologies
9.78 Dianomic Systems
9.79 Digital Reasoning Systems
9.80 Dimensional Insight
9.81 Dolphin Enterprise Solutions Corporation/Hanse Orga Group
9.82 Domino Data Lab
9.83 Domo
9.84 Dremio
9.85 DriveScale
9.86 Druva
9.87 Dundas Data Visualization
9.88 DXC Technology
9.89 Elastic
9.90 Engineering Group (Engineering Ingegneria Informatica)
9.91 EnterpriseDB Corporation
9.92 eQ Technologic
9.93 Ericsson
9.94 Erwin
9.95 EV (Big Cloud Analytics)
9.96 EXASOL
9.97 EXL (ExlService Holdings)
9.98 Facebook
9.99 FICO (Fair Isaac Corporation)
9.100 Figure Eight
9.101 FogHorn Systems
9.102 Fractal Analytics
9.103 Franz
9.104 Fujitsu
9.105 Fuzzy Logix
9.106 Gainsight
9.107 GE (General Electric)
9.108 Glassbeam
9.109 GoodData Corporation
9.110 Google/Alphabet
9.111 Grakn Labs
9.112 Greenwave Systems
9.113 GridGain Systems
9.114 H2O.ai
9.115 HarperDB
9.116 Hedvig
9.117 Hitachi Vantara
9.118 Hortonworks
9.119 HPE (Hewlett Packard Enterprise)
9.120 Huawei
9.121 HVR
9.122 HyperScience
9.123 HyTrust
9.124 IBM Corporation
9.125 iDashboards
9.126 IDERA
9.127 Ignite Technologies
9.128 Imanis Data
9.129 Impetus Technologies
9.130 Incorta
9.131 InetSoft Technology Corporation
9.132 InfluxData
9.133 Infogix
9.134 Infor/Birst
9.135 Informatica
9.136 Information Builders
9.137 Infosys
9.138 Infoworks
9.139 Insightsoftware.com
9.140 InsightSquared
9.141 Intel Corporation
9.142 Interana
9.143 InterSystems Corporation
9.144 Jedox
9.145 Jethro
9.146 Jinfonet Software
9.147 Juniper Networks
9.148 KALEAO
9.149 Keen IO
9.150 Keyrus
9.151 Kinetica
9.152 KNIME
9.153 Kognitio
9.154 Kyvos Insights
9.155 LeanXcale
9.156 Lexalytics
9.157 Lexmark International
9.158 Lightbend
9.159 Logi Analytics
9.160 Logical Clocks
9.161 Longview Solutions/Tidemark
9.162 Looker Data Sciences
9.163 LucidWorks
9.164 Luminoso Technologies
9.165 Maana
9.166 Manthan Software Services
9.167 MapD Technologies
9.168 MapR Technologies
9.169 MariaDB Corporation
9.170 MarkLogic Corporation
9.171 Mathworks
9.172 Melissa
9.173 MemSQL
9.174 Metric Insights
9.175 Microsoft Corporation
9.176 MicroStrategy
9.177 Minitab
9.178 MongoDB
9.179 Mu Sigma
9.180 NEC Corporation
9.181 Neo4j
9.182 NetApp
9.183 Nimbix
9.184 Nokia
9.185 NTT Data Corporation
9.186 Numerify
9.187 NuoDB
9.188 NVIDIA Corporation
9.189 Objectivity
9.190 Oblong Industries
9.191 OpenText Corporation
9.192 Opera Solutions
9.193 Optimal Plus
9.194 Oracle Corporation
9.195 Palantir Technologies
9.196 Panasonic Corporation/Arimo
9.197 Panorama Software
9.198 Paxata
9.199 Pepperdata
9.200 Phocas Software
9.201 Pivotal Software
9.202 Prognoz
9.203 Progress Software Corporation
9.204 Provalis Research
9.205 Pure Storage
9.206 PwC (PricewaterhouseCoopers International)
9.207 Pyramid Analytics
9.208 Qlik
9.209 Qrama/Tengu
9.210 Quantum Corporation
9.211 Qubole
9.212 Rackspace
9.213 Radius Intelligence
9.214 RapidMiner
9.215 Recorded Future
9.216 Red Hat
9.217 Redis Labs
9.218 RedPoint Global
9.219 Reltio
9.220 RStudio
9.221 Rubrik/Datos IO
9.222 Ryft
9.223 Sailthru
9.224 Salesforce.com
9.225 Salient Management Company
9.226 Samsung Group
9.227 SAP
9.228 SAS Institute
9.229 ScaleOut Software
9.230 Seagate Technology
9.231 Sinequa
9.232 SiSense
9.233 Sizmek
9.234 SnapLogic
9.235 Snowflake Computing
9.236 Software AG
9.237 Splice Machine
9.238 Splunk
9.239 Strategy Companion Corporation
9.240 Stratio
9.241 Streamlio
9.242 StreamSets
9.243 Striim
9.244 Sumo Logic
9.245 Supermicro (Super Micro Computer)
9.246 Syncsort
9.247 SynerScope
9.248 SYNTASA
9.249 Tableau Software
9.250 Talend
9.251 Tamr
9.252 TARGIT
9.253 TCS (Tata Consultancy Services)
9.254 Teradata Corporation
9.255 Thales/Guavus
9.256 ThoughtSpot
9.257 TIBCO Software
9.258 Toshiba Corporation
9.259 Transwarp
9.260 Trifacta
9.261 Unifi Software
9.262 Unravel Data
9.263 VANTIQ
9.264 VMware
9.265 VoltDB
9.266 WANdisco
9.267 Waterline Data
9.268 Western Digital Corporation
9.269 WhereScape
9.270 WiPro
9.271 Wolfram Research
9.272 Workday
9.273 Xplenty
9.274 Yellowfin BI
9.275 Yseop
9.276 Zendesk
9.277 Zoomdata
9.278 Zucchetti

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

 


List Of Figures


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

 


Global Big Data in Healthcare Market, By Component (Software & Service), By Software(Electronic Healthcare Record, Practice Management, Workforce Management), By Deployment (On-premise & Cloud), By Analytics Type (Descriptive; Predictive & Prescriptive), By Application (Financial Analytics, Clinical Data Analytics, & Operational Analytics), By End User, By Region, Competition, Forecast & Opportunities, 2024

Global Big Data in Healthcare Market, By Component (Software & Service), By Software(Electronic Healthcare Record, Practice Management, Workforce Management), By Deployment (On-premise & Cloud), By Analytics Type (Descriptive; Predictive & Prescriptive), By Application (Financial Analytics, Clinical Data Analytics, & Operational Analytics), By End User, By Region, Competition, Forecast & Opportunities, 2024 market research report available in single user pdf license with Aarkstore Enterprise at USD 4450

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Global Big Data in E-commerce Market Research Report 2020-2024

In the context of China-US trade war and global economic volatility and uncertainty, it will have a big influence on this market. Big Data in E-commerce Report by Material, Application,

USD 2850 View Report

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Global Big Data in Healthcare Market, By Component (Software & Service), By Software(Electronic Healthcare Record, Practice Management, Workforce Management), By Deployment (On-premise & Cloud), By Analytics Type (Descriptive; Predictive & Prescriptive), By Application (Financial Analytics, Clinical Data Analytics, & Operational Analytics), By End User, By Region, Competition, Forecast & Opportunities, 2024 market research report available in single user pdf license with Aarkstore Enterprise at USD 4450

USD 4450 View Report

Global Big Data in E-commerce Market Research Report 2020-2024

In the context of China-US trade war and global economic volatility and uncertainty, it will have a big influence on this market. Big Data in E-commerce Report by Material, Application,

USD 2850 View Report

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