Companies in the automotive industry can use data science and machine learning to optimize the quality of their products and processes across all business areas.
In the past, the automotive industry was an engine of technical innovation. In terms of digitization, however, it took a while for many original equipment manufacturers (OEMs) to start innovating in this area.
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.
Why you should Contact HData Systems for your automotive business
Our data science team has extensive industry experience in the automotive sector and supports you as a company in the automotive industry in the development, and implementation of data science and analytics projects in your company.
• Predictive maintenance: With predictive maintenance, you can identify defects before they occur and rectify them.
• Detection of anomalies: Automatically identify unusual or deviating data points in your quality data.
• Forecasting: Use machine learning and statistics to precisely forecast sales figures, revenues and costs.
• Root cause analysis: Investigate interdependencies in your data and use them to improve your products.
• Deep learning: Use modern deep learning methods to process image and text information.
• Pattern search: Automatically identify common damage or costs in your quality data.
Main benefits of data analytics in the automotive industry
• Improve product manufacturing
One of the most relevant reasons why companies decide to implement data analytics in the automotive sector is to improve the quality of their products. This is essential because consumers mostly demand durability and safety in vehicles.
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.
• It helps improve loyalty
The big automotive data collected large amounts of information, mainly related to users. But its greatest advantage is that it is possible to cross all this data with other external ones, such as geographic or social, even through Artificial Intelligence.
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.
• The design of new models
Thanks to data analytics in the automotive sector, both internal and external information is obtained that helps to optimize the production process. On the one hand, the data processing of the company's clients is carried out, and on the other, comments from social networks and the internet can be collected.
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.
Use HData Solutions to make data-based decision in your automotive business
At HData Systems, we carry out data processing adapted to the needs of your automobile company in order to help you understand the market and segment your target audience. In this way, you will be able to know the tastes and preferences of your customers, and thus, focus the advertising campaigns appropriately and orient your services to them. And, there is nothing like having the best information to make better decisions.
Some of our top technologies for data analytics
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Data quality
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Data preparation
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Data integration
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Data virtualization
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Distributed file stores
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In-Memory data fabric
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Stream analytics
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Knowledge discovery and search
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NoSQL
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Predictive analytics
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