The mobility company Cooltra has launched an ambitious technological project with the collaboration of IBM to improve fleet management of its connected motorcycles. This initiative relies on the watsonx.data data platform to analyze information in near real time and prepare future use cases based on artificial intelligence, thus reinforcing its intelligent fleet management model.
Cooltra manages a fleet of more than 16,000 motorcycles and operates in key markets such as Spain, Portugal, the Netherlands, France and Italy, placing itself among the leading European motosharing operators by size and scope. In this context, having a robust data platform is essential to optimize fleet management in connected and geographically distributed environments.
To respond to these needs, the unified platform developed together with IBM incorporates advanced capabilities for fleet management, such as vehicle behavior analysis, operational observability and early incident detection. In addition, it provides a solid foundation for the development of advanced analytics and artificial intelligence applied to fleet management.
IBM watsonx.data advanced platform
Cooltra relies on watsonx.data, IBM’s data platform, as the core of its fleet management system. This solution allows you to store and process events and telemetric data from thousands of vehicles in almost real time, consolidating key information such as motorcycle status, usage, battery, location and other operational events relevant to fleet management.
By analyzing this historical data, the company can apply predictive maintenance across its fleet, significantly improving fleet management performance, availability and efficiency.
“Having a robust and scalable data platform is a key step to evolve our fleet management model,” says Miguel Vera, Telematics IT Manager at Cooltra. “It allows us to work with data in near real time and pave the way for new services based on artificial intelligence that optimize fleet management and our customers’ experience.”
For his part, Iñigo Cavestany, Data Build Seller at IBM, highlights: “In mobility companies like Cooltra, the value of AI is not in the algorithm, but in the quality and architecture of the data applied to fleet management. When working with thousands of connected vehicles, it is essential to have a platform capable of processing information in real time, contextualizing it and transforming it into operational decisions that improve fleet management. This project responds precisely to that objective.”
Data architecture based on open ecosystem
The solution deployed for Cooltra is based on a SaaS architecture in the cloud, implemented on AWS, which combines IBM’s own technologies with solutions from its ecosystem. In this environment, AstraDB (DataStax)—recently incorporated into the IBM portfolio—acts as a database for storing events and telematics data generated by the fleet, which are subsequently integrated into watsonx.data to reinforce fleet management.
This approach reflects IBM’s commitment to an open ecosystem, which allows it to offer greater flexibility, interoperability and scalability in fleet management projects. Additionally, the company continues to expand its portfolio through strategic acquisitions that provide key capabilities to build more robust data architectures.
Advanced analytics and AI projects
The implementation of this platform represents a significant advance in the technological evolution of Cooltra. By having an architecture capable of integrating and analyzing data in almost real time, the company establishes a solid foundation to promote new projects aimed at improving fleet management through advanced analytics and artificial intelligence.
This approach allows you to optimize operations, improve service availability and reinforce decision-making at all levels of the organization, with a direct impact on fleet management.
Thanks to the analysis of historical data, the company can apply predictive maintenance throughout its fleet
Based on this technological base, Cooltra is developing new optimization initiatives, including:
- Improvement of fleet management at an operational level.
- Anomaly detection and predictive maintenance through artificial intelligence applied to fleet management.
- Identification of unusual patterns or device failures, with automatic generation of alerts for better fleet management.
- Optimization of motorcycle parameters, identifying areas with less coverage to adjust waiting times within fleet management.
- Route optimization to increase the overall efficiency of fleet management.
From IBM, this project highlights the strategic role of data platforms as a driver of digital transformation and as a key element to promote new use cases aimed at improving fleet management in mobility companies.
