In a world where big data is an invaluable asset for organizations, a leading car manufacturer company decided to rely on Technology Reply's expertise to enrich its new analytics platform, with useful information for data analysis related to the areas of autonomous driving, smart cockpit and connected vehicle services.
The analytics platform will enable the study and analysis of driving style, through significant key indicators.
Through the signals coming from sensors placed in the vehicles, a large amount of data can be collected, providing visibility of the use of the cars in terms of usage and activity times; relevant information for undertaking advanced predictive maintenance and driving behavior analyses. Through such analyses, the identification of the real needs of drivers can be identified in order to provide them with the most suitable services.
The solution implemented on the new platform includes, in addition to the process of Extraction, Transformation and Loading (ETL) of the data on various layers of the lakehouse, the enrichment of the information already presents on the new platform.
Until now in fact, the current platform had only the data related to the vehicle, without product and market type details.
Through the migration and exposure of data on the new platform, it will now be possible to integrate the vehicle data with the related product and market master data, in this way the end user could perform deeper analysis with greater levels of detail.
Moreover, in addition to the implementation of the back-end solution, Technology Reply will be responsible for designing and developing a front-end solution, for the creation of dashboards dedicated to the visualization of reports and key indicators useful for the business users analysis.
The project is divided into four macro areas:
The technologies used are the following ones:
The main challenge concerned the management of a huge amount of data that is sent daily in near real time from the sensors placed inside the vehicles. Technology Reply was able to meet this challenge through the study of a solution based on data aggregation logics aimed to performance optimization of the ascent on the different layers, all leveraging the new Cloud-based tool, adopted specifically for large amount of data processing and transforming.