The areas of application of data science and machine learning in the automotive sector are almost limitless. Starting with the modeling and forecasting of quality problems, through the application of scoring and forecasting models in the aftersales area, to the implementation of complex deep learning models in the field of autonomous driving, many things seem possible and sensible.

• Pattern search: Automatically identify common damage or costs in your quality data.

Data analytics and big data has been used for years in assembly plants to establish quality standards, where it is possible to predict the duration of the different parts of the vehicle. It makes it is possible to detect where the motor or electrical system is failing, which helps to introduce the necessary changes in the assembly line to be able to make the necessary corrections.

Thanks to data processing, companies can reach the most interesting conclusions. On the one hand, the internal processing allows obtaining information about the company itself. On the other hand, external processing allows access to data about the target audience, among others, to be able to define the behavior or motivations of the user of a certain territory.

Thus, once both slopes have been crossed, the automobile company is able to adapt its production based on these data-based variables. Despite this, it is necessary to order the information correctly and have a professional with analytical capacity who knows how to interpret the data to turn it into knowledge, such as knowing which finishes would be sold the most, which accessories users demand the most or what type of vehicle would prefer each segment.