Automotive data science

Automotive data science is the application of data science techniques to the automotive industry. It involves the collection, analysis, and interpretation of data to improve the efficiency, safety, and performance of vehicles.  

Over the years, data science has had a significant impact on the automotive industry.  One of the key changes that happened is, earlier the industry relied on business intelligence to analyse sales data, customer feedback, and other metrics to improve business operations. However, with the advent of advanced analytical techniques like predictive analytics, the automotive industry has started using data to predict future trends and outcomes. Advanced analytics has helped automakers develop more efficient and reliable vehicles, tailor their products to meet the needs of their target audience and improve manufacturing efficiency. 

Let’s look at recent trends in automotive data science in more detail.  

Predictive analytics in Automotive Data  

One of the biggest trends in automotive data science is the use of predictive analytics. Predictive analytics uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. In the automotive industry, predictive analytics is being used to predict when vehicles need maintenance, which helps prevent surprise repairs and downtime for the driver, and also extends the lifespan of components in the vehicle. 

Deep Learning  

Another trend is the use of deep learning vehicular human-friendly models. The aim is to create a model that can learn from data and make decisions that are more human-like and understandable. This is especially important for autonomous vehicles, where the vehicle must be able to make decisions that are safe and understandable to passengers. 

Digital Twins  

A third trend is the use of digital twins. A digital twin is a virtual replica of a physical object or system that can be used to simulate and analyse its behaviour. In the automotive industry, digital twins are being used to simulate the performance of vehicles and their components, which can help optimise their design and reduce the time and cost of physical testing. 

Machine Learning in Automotive Data 

Lastly, machine learning is another recent trend in the industry. As we know, machine learning is a subset of AI that involves training algorithms to make predictions or decisions based on data. In the automotive industry, machine learning is being used to improve the accuracy of predictive maintenance and to develop autonomous vehicles. 

Applications of advanced tech in the automotive sector 

Aside from these, there are other applications of data science in this sector as well. For one, data science is being used to develop new products and improve existing ones. For example, machine learning and artificial intelligence are being used in the automotive industry loans. Data science is also being used to analyse car model configurations, component reliability, and conduct testing in small scales before implementing at the manufacturing level. Secondly, data science is being used to understand consumer behaviour and preferences. For instance, data science is being used to predict customer behaviours and improve the customer journey. According to a recent survey, 37% of consumers would switch car brands to achieve improvements in connectivity. 

What We Can Expect in the Future 

The automotive industry is one of the oldest industries in the world, but it’s much more than just making cars. Because mobility is a part of our everyday lives, companies in this industry have a huge reach and massive impact on society.  

Every single business function from manufacturing to mobile application development exists within the automotive industry. And, data science is going to continue to drive the future of this industry. According to McKinsey & Company, we can see four disruptive technology-driven trends in the automotive sector: diverse mobility, autonomous driving, electrification, and connectivity. These trends will reinforce and accelerate one another. 

In addition to these trends, there are several other data trends that will drive the future of the automotive industry. Here are some of them: 

  1. Capturing customer data: Customer experience (CX) is fast becoming the largest differentiator between brands in any industry, including the automotive sector. Companies will begin to capture more customer data to improve CX and gain a competitive edge. 
  1. Automotive app ecosystems: Automotive app ecosystems are emerging as a new trend in the industry. These ecosystems will allow developers to create apps that can be used in cars, providing drivers with a more personalised experience. 
  1. Harnessing electrical infrastructures: The automotive industry is moving towards electrification, and companies will harness electrical infrastructures to improve the efficiency of electric vehicles. 
  1. Automotive Big Data: The amount of data generated by connected cars is enormous, and companies will use this data to improve safety, efficiency, and customer experience. 
  1. Mobility-as-a-service: Mobility-as-a-service is a new trend that is emerging in the automotive industry. It involves providing customers with a range of transportation options, including ride-sharing, car-sharing, and public transportation. 
  1. Staying cyber secure: As cars become more connected, they become more vulnerable to cyber attacks. Companies will invest more resources in cybersecurity to ensure that their vehicles are secure. 
  1. Self-driving vehicles: Self-driving vehicles are the future of the automotive industry. Companies will invest heavily in this technology to improve safety, efficiency, and customer experience. 

These trends are just the tip of the iceberg, and we can expect data science to continue to drive innovation and shape the future of the automotive industry. Companies will continue to scale data science applications on everything from small-sized data to hundred-billion row intelligent connected vehicle systems. Applying data science use cases in a human-centric fashion will have a lasting impact on society at large. 

Merit’s Expertise in Data Aggregation & Harvesting for the Global Automotive Sector 

Merit Data and Technology excels in aggregating and harvesting automotive data using AI, ML, and human expertise. Our capabilities include: 

  • Crafting end-to-end data pipelines and scalable data warehouses 
  • Designing compliant governance solutions for seamless integration 
  • Utilising high-volume, high-velocity data tools for nuanced insights 
  • Extracting retail product attributes and audience data 
  • Aggregating industry-specific data points for informed decision-making 

Trusted by leading automotive brands, Merit drives innovation and efficiency by delivering refined, actionable insights.

Key Takeaways 

Predictive Analytics Dominates: One of the standout trends in automotive data science is the widespread adoption of predictive analytics. This technique, leveraging data, statistical algorithms, and machine learning, enables the industry to foresee maintenance needs in vehicles, preventing surprise breakdowns and extending the lifespan of vehicle components. 

Human-Friendly AI Models for Autonomous Vehicles: The development of deep learning vehicular human-friendly models is a crucial trend, especially in the realm of autonomous vehicles. This involves creating models that mimic human decision-making, ensuring safety and comprehension for passengers. This shift is pivotal for the acceptance and success of self-driving vehicles. 

Rise of Digital Twins and Simulation: Digital twins, virtual replicas used to simulate and analyse the behaviour of physical objects or systems, are gaining traction in the automotive industry. They are instrumental in optimising vehicle design, reducing the need for extensive physical testing. Simulation techniques, utilising mathematical models, are also becoming commonplace, aiding in predicting vehicle performance and streamlining design processes. 

Diverse Trends in Future Applications: Looking ahead, diverse trends in data science applications are expected to shape the automotive industry. These include the increasing focus on capturing customer data to enhance the customer experience, the emergence of automotive app ecosystems for personalised in-car experiences, the harnessing of electrical infrastructures for improved efficiency in electric vehicles, and the utilisation of Big Data generated by connected cars to enhance safety, efficiency, and customer experience.

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