Making Connected Car Data Matter

Making Connected Car Data Matter means a commitment to create technology that has a meaningful impact on people, businesses, and society. It emphasises the company's focus on delivering not just technology for technology's sake, but solutions that genuinely improve and enrich lives and operations.

In this article, we are going to discuss.

• Automotive OEMs' race towards many new features and lag in realising data benefits.
• After collecting decade-old, connected car data in the cloud why did OEMs fail to utilise its fullest benefits?
• Making Connected Car Data Matters through new revenue stream and reducing costs

Cluster of Connected Data and Analytics Inference
 

Connected cars collect a broad array of data to enhance driving experiences, improve safety, and enable various connected services. Those data can be broadly classified under the below 7 categories. Here's a closer look at the types of data these vehicles can collect:

1. Location Data

2. Performance Data

3. Driver Behaviour Related Data

4. Environmental Data

5. Infotainment Usage

6. Safety and Security Data

7. Telematics Data

Data Analytics
 

Data analytics plays a crucial role in extracting meaningful insights from the vast amounts of data collected by connected cars. By applying various data analysis techniques, automakers, service providers, and other stakeholders can gain valuable insights to improve vehicle performance, enhance safety, personalise user experiences, and open new business opportunities.

1. Location Data

Traffic Management: Analytics can predict traffic conditions and suggest optimal routes in real time, reducing travel times and avoiding congestion. [ REVENUE – by DATA SHARING to map & Service providers]

Geofencing: Analysis of location data enables services like automated toll payments, location-based alerts, or theft prevention through geofencing. [ REVENUE – from Customers for value-added Services]

2. Performance Data

Predictive Maintenance: By analysing performance data, predictive models can identify potential issues before they occur, scheduling maintenance only when necessary and avoiding unnecessary service costs. [ Reducing cost]

Fuel Efficiency Optimization: Data analytics can identify patterns in fuel consumption to provide drivers with recommendations for improving fuel efficiency. [ Reducing cost]

3. Driver Behaviour

Insurance Risk Assessment: Insurers use analytics to assess driver risk profiles based on driving behaviour, offering personalised insurance premiums (usage-based insurance) [ REVENUE – by partnering with insurance provider]

Safety Enhancements: Analysis of driver behaviour data can help in developing advanced driver-assistance systems (ADAS) that improve safety by alerting drivers to risky behaviours.[ Product value add ]

4. Environmental Data

Vehicle Performance Adjustment: Analytics can use environmental data to adjust vehicle settings in real time for optimal performance, such as changing the suspension setup based on road conditions or adjusting the battery management system in electric vehicles based on temperature. [ sustainability & Customer cost reduction]

5. Infotainment Usage

Personalisation: Analytics helps in personalising the infotainment offerings based on user preferences, and suggesting media or apps based on past usage. [ REVENUE – personalised Advertisements]

6. Safety and Security Data

Incident Analysis: Post-accident data analysis can provide insights into common factors contributing to accidents, informing new safety features or improvements in vehicle design.

Security Monitoring: Analysing security data can help in detecting patterns that might indicate a security breach, improving anti-theft measures and cybersecurity protocols.

7. Telematics Data

Fleet Management: For commercial fleets, analytics can optimise routes, reduce fuel consumption, and manage vehicle health across the fleet, improving efficiency and reducing operational costs. [ REVENUE – Shared mobility]

Usage-Based Models: Analysis of telematics data supports innovative business models, such as pay-per-use leasing or insurance, by providing a detailed understanding of vehicle usage patterns. [ REVENUE – Shared mobility]

Growth Potential and Roadblocks:
 

Growth Opportunities: 

McKinsey projected 36-41% of overall Automotive revenue will come from recurring revenue models through Shared mobility and car data-enabled Services.

Making revenue:
 

• Usage-Based Insurance (UBI): Insurance premiums are calculated based on actual driving behaviour, rewarding safer driving habits with lower rates.
• Data Monetisation: Aggregated vehicle data can be used to inform urban planning, retail, and other industries, creating new revenue streams.
• Personalized Advertisement in Infotainment

 
Reducing Costs

Leveraging product field data for R&D and material cost reduction involves collecting and analysing real-world usage data to decrease development expenses and refine materials and designs according to the product's actual service life. This approach aims at:

R&D and Material Cost Reduction:

Streamlining Development Costs: Utilising real-world product data to minimise unnecessary expenditures in the development phase by aligning design and material choices with actual usage requirements.

Optimising Materials and Designs: Enhancing product designs and material selections to extend service life and performance, directly influencing cost efficiency and sustainability.

Reducing Customer Costs

Usage, Repair, and Downtime Cost Reduction: By understanding patterns in how products or services are used, strategies can be developed to lower the costs associated with usage, maintenance, and potential downtimes for customers.

Overcoming Roadblocks:

Overcoming the challenges associated with the rapid growth of connected car technologies and Data requires key strategies:

1. Investing in Cybersecurity

Data Encryption and Anonymisation: Ensure that data transmitted to and from the vehicle is encrypted and that personally identifiable information is anonymised to protect user privacy.

2. Adhering to Regulatory Standards

Compliance with Data Protection Laws: Follow global and local regulations regarding data privacy, such as GDPR in Europe, to ensure the lawful processing of personal data.

3. Fostering Industry Collaboration

Partnerships Between Automakers and Tech Companies: Encourage collaborations that leverage the strengths of both sectors in developing innovative solutions.

4. Innovative Business Models

Each company is unique, I will leave it to the respective company to decide this. But the conventional Business model will take nowhere for a Software-defined vehicle to produce billions of data soon.

Steps to Make It Matter:
 

STEP 1: Effective Data Management
 

Effective data management strategies are crucial in the context of connected cars, where vast volumes of diverse data are generated continuously. These strategies ensure that data is leveraged safely, efficiently, and ethically to deliver value to users, manufacturers, and other stakeholders.

Confidence in data quality will give more freedom to OEMs to take the next steps. Following effective Data management practices will enable Quality Data storage.

1. Data Storage with Scalability, Accessibility& Long-Term Retention in mind

2. Data Processing

Real-Time Analytics: Necessary for immediate feedback on critical functionalities.

Efficient Processing: Optimises handling of large data sets with minimal latency.

Integration Capabilities: Facilitates seamless data integration with external systems.

3. Data Security

Privacy Protection: Critical to safeguard user privacy and sensitive information.

Data Integrity: Ensures data accuracy and prevents malicious manipulation.

Regulatory Compliance: Mandatory to meet legal standards and avoid penalties.

STEP 2: Leveraging Advanced Analytics:
 

Advanced analytics and Artificial Intelligence (AI) play a pivotal role in transforming vast datasets collected from connected cars into actionable insights. These technologies are at the heart of making sense of complex, high-volume data in real time, enabling smarter decisions, enhancing user experiences, and improving vehicle performance and safety.

Pattern Recognition: To identify trends for personalised experiences and optimised navigation.

Predictive Analytics: For forecasting maintenance needs and enhancing vehicle reliability.

Optimization Algorithms: Improves resource allocation, like battery and fuel usage.

AI enhances Analytics capabilities through ML, NLP and Computer Vision[S4] [MM5] .

Machine Learning (ML): Improves accuracy in predictive maintenance and behaviour analysis over time.

Natural Language Processing (NLP): Facilitates intuitive voice interactions.

Computer Vision: Powers safety features by analysing visual data for obstacle detection and automated driving aids.

STEP 3: Integration with Other Systems:
 

Integrating connected car data with traffic management and smart city infrastructure represents a paradigm shift towards more interconnected, intelligent urban ecosystems. It leverages the power of real-time data analytics to improve urban mobility, enhance safety, and support sustainable urban development, illustrating the transformative potential of this integration in shaping the future of cities.

Conclusion

In conclusion, making connected car data matter is about harnessing this rich data source to deliver tangible benefits. By addressing the technical and regulatory challenges and focusing on areas of growth, the automotive industry can drive forward innovations that not only transform the driving experience but also contribute positively to the broader societal and urban landscape.