Analysis of Potential Impact of COVID-19 on Big Data in the Automotive Market Opportunities : Biggest Innovation to Boost Global Growth Rate till 2025
“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.
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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.
The report covers the following topics:
- Big Data ecosystem
- Market drivers and barriers
- Enabling technologies, standardization and regulatory initiatives
- Big Data analytics and implementation models
- Business case, key applications and use cases in the automotive industry
- 30 case studies of Big Data investments by automotive OEMs and other stakeholders
- Future roadmap and value chain
- Company profiles and strategies of over 240 Big Data vendors
- Strategic recommendations for Big Data vendors, automotive OEMs and other stakeholders
- Market analysis and forecasts from 2017 till 2030
Forecast Segmentation
Market forecasts are provided for each of the following submarkets and their subcategories:
Hardware, Software & Professional Services
- Hardware
- Software
- Professional Services
Horizontal Submarkets
- Storage & Compute Infrastructure
- Networking Infrastructure
- Hadoop & Infrastructure Software
- SQL
- NoSQL
- Analytic Platforms & Applications
- Cloud Platforms
- Professional Services
Application Areas
- Product Development, Manufacturing & Supply Chain
- After-Sales, Warranty & Dealer Management
- Connected Vehicles & Intelligent Transportation
- Marketing, Sales & Other Applications
Use Cases
- Supply Chain Management
- Manufacturing
- Product Design & Planning
- Predictive Maintenance & Real-Time Diagnostics
- Recall & Warranty Management
- Parts Inventory & Pricing Optimization
- Dealer Management & Customer Support Services
- UBI (Usage-Based Insurance)
- Autonomous & Semi-Autonomous Driving
- Intelligent Transportation
- Fleet Management
- Driver Safety & Vehicle Cyber Security
- In-Vehicle Experience, Navigation & Infotainment
- Ride Sourcing, Sharing & Rentals
- Marketing & Sales
- Customer Retention
- Third Party Monetization
- Other Use Cases
Regional Markets
- Asia Pacific
- Eastern Europe
- Latin & Central America
- Middle East & Africa
- North America
- Western Europe
Country Markets
- Argentina, Australia, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, India, Indonesia, Israel, Italy, Japan, Malaysia, Mexico, Netherlands, Norway, Pakistan, Philippines, Poland, Qatar, Russia, Saudi Arabia, Singapore, South Africa, South Korea, Spain, Sweden, Taiwan, Thailand, UAE, UK, USA
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Key Questions Answered
The report provides answers to the following key questions:
- How big is the Big Data opportunity in the automotive industry?
- How is the market evolving by segment and region?
- What will the market size be in 2020 and at what rate will it grow?
- What trends, challenges and barriers are influencing its growth?
- Who are the key Big Data software, hardware and services vendors and what are their strategies?
- How much are automotive OEMs and other stakeholders investing in Big Data?
- What opportunities exist for Big Data analytics in the automotive industry?
- Which countries, application areas and use cases will see the highest percentage of Big Data investments in the automotive industry?
Key Findings
The report has the following key findings:
- In 2017, Big Data vendors will pocket over $2.8 Billion from hardware, software and professional services revenues in the automotive industry. These investments are further expected to grow at a CAGR of approximately 12% over the next three years, eventually accounting for over $4 Billion by the end of 2020.
- In a bid to improve customer retention, automotive OEMs are heavily relying on Big Data and analytics to integrate an array of data-driven aftermarket services such as predictive vehicle maintenance, real-time mapping and personalized concierge services.
- In recent years, several prominent partnerships and M&A deals have taken place that highlight the growing importance of Big Data in the automotive industry. For example, tier-1 supplier Delphi recently led an investment round to raise over $25 Million for Otonomo, a startup that has developed a data exchange and marketplace platform for vehicle-generated data.
- Addressing privacy concerns is necessary in order to monetize the swaths of Big Data that will be generated by a growing installed base of connected vehicles and other segments of the automotive industry.
List of Companies Mentioned
- 1010data
- Absolutdata
- Accenture
- ACEA (European Automobile Manufacturers’ Association)
- Actian Corporation
- Adaptive Insights
- Advizor Solutions
- AeroSpike
- AFS Technologies
- Alation
- Algorithmia
- Alibaba
- Alliance of Automobile Manufacturers
- Alluxio
- Alphabet
- Alpine Data
- Alteryx
- AMD (Advanced Micro Devices)
- Apixio
- Arcadia Data
- Arimo
- ARM
- ASF (Apache Software Foundation)
- AtScale
- Attivio
- Attunity
- Audi
- Automated Insights
- automotiveMastermind
- AWS (Amazon Web Services)
- Axiomatics
- Ayasdi
- Basho Technologies
- BCG (Boston Consulting Group)
- Bedrock Data
- BetterWorks
- Big Cloud Analytics
- Big Panda
- BigML
- Birst
- Bitam
- Blue Medora
- BlueData Software
- BlueTalon
- BMC Software
- BMW
- BOARD International
- Booz Allen Hamilton
- Boxever
- CACI International
- Cambridge Semantics
- Capgemini
- Cazena
- Centrifuge Systems
- CenturyLink
- Chartio
- Cisco Systems
- Civis Analytics
- ClearStory Data
- Cloudability
- Cloudera
- Clustrix
- CognitiveScale
- Collibra
- Concurrent Computer Corporation
- Confluent
- Contexti
- Continental
- Continuum Analytics
- Couchbase
- CrowdFlower
- CSA (Cloud Security Alliance)
- CSCC (Cloud Standards Customer Council)
- Daimler
- Dash Labs
- Databricks
- DataGravity
- Dataiku
- Datameer
- DataRobot
- DataScience
- DataStax
- DataTorrent
- Datawatch Corporation
- Datos IO
- DDN (DataDirect Networks)
- Decisyon
- Dell EMC
- Dell Technologies
- Deloitte
- Delphi Automotive
- Demandbase
- Denodo Technologies
- Denso Corporation
- Digital Reasoning Systems
- Dimensional Insight
- DMG (Data Mining Group)
- Dolphin Enterprise Solutions Corporation
- Domino Data Lab
- Domo
- DriveScale
- Dundas Data Visualization
- DXC Technology
- Eligotech
- Engie
- Engineering Group (Engineering Ingegneria Informatica)
- EnterpriseDB
- eQ Technologic
- Ericsson
- EXASOL
- 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
- Greenwave Systems
- GridGain Systems
- Groupe PSA
- Groupe Renault
- Guavus
- H2O.ai
- HDS (Hitachi Data Systems)
- Hedvig
- HERE
- Honda Motor Company
- Hortonworks
- HPE (Hewlett Packard Enterprise)
- Huawei
- Hyundai Motor Company
- IBM Corporation
- iDashboards
- IEC (International Electrotechnical Commission)
- IEEE (Institute of Electrical and Electronics Engineers)
- Impetus Technologies
- INCITS (InterNational Committee for Information Technology Standards)
- Incorta
- InetSoft Technology Corporation
- Infer
- Infor
- Informatica Corporation
- Information Builders
- Infosys
- Infoworks
- Insightsoftware.com
- InsightSquared
- Intel Corporation
- Interana
- InterSystems Corporation
- ISO (International Organization for Standardization)
- Jaguar Land Rover
- Jedox
- Jethro
- Jinfonet Software
- Juniper Networks
- KALEAO
- KDDI Corporation
- Keen IO
- Kia Motor Corporation
- Kinetica
- KNIME
- Kognitio
- Kyvos Insights
- Lavastorm
- Lexalytics
- Lexmark International
- Lexus
- Linux Foundation
- Logi Analytics
- Longview Solutions
- Looker Data Sciences
- LucidWorks
- Luminoso Technologies
- Lytx
- Maana
- Magento Commerce
- Manthan Software Services
- MapD Technologies
- MapR Technologies
- MariaDB Corporation
- MarkLogic Corporation
- Mathworks
- Mazda Motor Corporation
- MemSQL
- Mercedes-Benz
- METI (Ministry of Economy, Trade and Industry, Japan)
- Metric Insights
- Michelin
- Microsoft Corporation
- MicroStrategy
- Minitab
- MongoDB
- Mu Sigma
- NEC Corporation
- Neo Technology
- NetApp
- Nimbix
- Nissan Motor Company
- NIST (U.S. National Institute of Standards and Technology)
- Nokia
- NTT Data Corporation
- NTT Group
- Numerify
- NuoDB
- Nutonian
- NVIDIA Corporation
- NYC DOT (New York City Department of Transportation)
- OASIS (Organization for the Advancement of Structured Information Standards)
- Oblong Industries
- ODaF (Open Data Foundation)
- ODCA (Open Data Center Alliance)
- ODPi (Open Ecosystem of Big Data)
- OGC (Open Geospatial Consortium)
- OpenText Corporation
- Opera Solutions
- Optimal Plus
- Oracle Corporation
- Otonomo
- Palantir Technologies
- Panorama Software
- Paxata
- Pentaho Corporation
- Pepperdata
- Phocas Software
- Pivotal Software
- Prognoz
- Progress Software Corporation
- PwC (PricewaterhouseCoopers International)
- Pyramid Analytics
- Qlik
- Quantum Corporation
- Qubole
- Rackspace
- Radius Intelligence
- RapidMiner
- Recorded Future
- Red Hat
- Redis Labs
- RedPoint Global
- Reltio
- Robert Bosch
- Rocket Fuel
- Rosenberger
- RStudio
- Ryft Systems
- SAIC Motor Corporation
- Sailthru
- Salesforce.com
- Salient Management Company
- Samsung Group
- SAP
- SAS Institute
- ScaleDB
- ScaleOut Software
- SCIO Health Analytics
- Seagate Technology
- Sinequa
- SiSense
- SnapLogic
- Snowflake Computing
- Software AG
- Splice Machine
- Splunk
- Sqrrl
- Strategy Companion Corporation
- StreamSets
- Striim
- Subaru
- Sumo Logic
- Supermicro (Super Micro Computer)
- Suzuki Motor Corporation
- Syncsort
- SynerScope
- Tableau Software
- Talena
- Talend
- Tamr
- TARGIT
- TCS (Tata Consultancy Services)
- Teradata Corporation
- Tesla
- The Floow
- ThoughtSpot
- THTA (Tokyo Hire-Taxi Association)
- TIBCO Software
- Tidemark
- TM Forum
- Toshiba Corporation
- Toyota Motor Corporation
- TPC (Transaction Processing Performance Council)
- Trifacta
- Uber Technologies
- Unravel Data
- Valens
- VMware
- Volkswagen Group
- VoltDB
- Volvo Cars
- W3C (World Wide Web Consortium)
- Waterline Data
- Western Digital Corporation
- WiPro
- Workday
- Xevo
- Xplenty
- Yellowfin International
- Yseop
- Zendesk
- Zoomdata
- Zucchetti
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Table of Contents
1 Chapter 1: Introduction 22
1.1 Executive Summary 22
1.2 Topics Covered 24
1.3 Forecast Segmentation 25
1.4 Key Questions Answered 28
1.5 Key Findings 29
1.6 Methodology 30
1.7 Target Audience 31
1.8 Companies & Organizations Mentioned 32
2 Chapter 2: An Overview of Big Data 36
2.1 What is Big Data? 36
2.2 Key Approaches to Big Data Processing 36
2.2.1 Hadoop 37
2.2.2 NoSQL 39
2.2.3 MPAD (Massively Parallel Analytic Databases) 39
2.2.4 In-Memory Processing 40
2.2.5 Stream Processing Technologies 40
2.2.6 Spark 41
2.2.7 Other Databases & Analytic Technologies 41
2.3 Key Characteristics of Big Data 42
2.3.1 Volume 42
2.3.2 Velocity 42
2.3.3 Variety 42
2.3.4 Value 43
2.4 Market Growth Drivers 44
2.4.1 Awareness of Benefits 44
2.4.2 Maturation of Big Data Platforms 44
2.4.3 Continued Investments by Web Giants, Governments & Enterprises 45
2.4.4 Growth of Data Volume, Velocity & Variety 45
2.4.5 Vendor Commitments & Partnerships 45
2.4.6 Technology Trends Lowering Entry Barriers 46
2.5 Market Barriers 46
2.5.1 Lack of Analytic Specialists 46
2.5.2 Uncertain Big Data Strategies 46
2.5.3 Organizational Resistance to Big Data Adoption 47
2.5.4 Technical Challenges: Scalability & Maintenance 47
2.5.5 Security & Privacy Concerns 47
3 Chapter 3: Big Data Analytics 49
3.1 What are Big Data Analytics? 49
3.2 The Importance of Analytics 49
3.3 Reactive vs. Proactive Analytics 50
3.4 Customer vs. Operational Analytics 51
3.5 Technology & Implementation Approaches 51
3.5.1 Grid Computing 51
3.5.2 In-Database Processing 52
3.5.3 In-Memory Analytics 52
3.5.4 Machine Learning & Data Mining 52
3.5.5 Predictive Analytics 53
3.5.6 NLP (Natural Language Processing) 53
3.5.7 Text Analytics 54
3.5.8 Visual Analytics 55
3.5.9 Graph Analytics 55
3.5.10 Social Media, IT & Telco Network Analytics 56
Continue…
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