Fleet management has long been limited to locating vehicles. With Big Data, this sector is greatly disrupted and opens up new horizons for businesses. When we talk about fleet management, it usually means managing dozens of vehicles simultaneously. For example, an administrator can take care of a fleet of cars, boats, trucks or a complete supply chain integrating several types of vehicles.

Generally speaking, fleet management aims to optimize vehicle use, reduce costs and environmental impact, and improve driver safety and end customer service. Ultimately, good fleet management enhances the competitiveness of your business. Historically, fleet management has been used to locate vehicles. In a supply chain context, this facilitates the tracking of goods. This allows in particular to indicate to a customer where his order is. This localization can be carried out in real-time or in deferred mode according to the needs of the company.

By crossing data from vehicles with other information in real-time, big data opens up new perspectives for fleet managers. The challenge? Improve fleet management and reduce its operating cost. This translates into many daily benefits.

• A better-maintained fleet: thanks to data from vehicles, it is now possible to assess the state of the vehicle in real-time. Alerts can also be sent to notify the manager in the event of a need for maintenance or repair.
• Optimized routes: thanks to the large amount of data collected in real-time (weather conditions, traffic, etc.), fleet management also makes it possible to choose the best routes at every given moment, which can achieve significant productivity gains.
• Improved purchasing and resale: thanks to big data, managers can now keep track of the breakdown and maintenance rates of vehicles, which can effectively guide the company's purchasing policy. It is also a great tool when reselling vehicles, as it allows you to know the most used vehicles as well as the popular sales channels.
• More virtuous driving: the data collected on the use of vehicles (speed, hard braking, use of indicators, etc.) offer the fleet manager the opportunity to analyze the quality of employees driving. A solution to encourage drivers to adopt a more virtuous behavior, in particular with the aim of reducing accidents and fuel consumption.

Data Overwhelms Fleet Managers

Techniques and applications have evolved. From software updates to technical diagnostics and even the implementation of business applications, fleet management systems are ingesting more and more data. In itself, the system can perform the same tasks, but the platforms in question are not necessarily designed to ingest, process, and analyze this information.

Fleet Management: Important Technical Challenges

The specialists in this field therefore tackle this issue head on and adapt their solutions to their customers' requests. Fleet management platform editors are relying on the Cloud, strong integration of Big Data and real-time analytics. However, all these technologies require a high level of interoperability. This is why publishers are turning to open source technologies. All that is needed is a web server and a DBMS (database management system) to set them up as part of a GPL Open Source IT Equipment Manager solution, that is, to manage from the web. Otherwise, you have to integrate the different APIs corresponding to the complementary tools. To do this, the data should be standardized so that their integration into the system runs smoothly. In addition, the data itself must be assimilable whether it is structured or not. The openness of ecosystems is essential for the success of the “Big Data” transformation of fleet management tools. Shells enable you to run these applications in a secure environment without any restrictions.

Identifying the Right Data

Telematics equipment, vehicles or any other sensor collect an impressive amount of data. Not all this data is necessary to manage a fleet of vehicles, for example, and it is important to choose the one that will transform and positively impact your business. This is called Smart Data. The other technical challenge faced by publishers and their customers is Regulatory data protection laws such as the european GDPR. End users should be made aware that some of their personal data, especially in the context of vehicle insurance, is shared with and processed by different subcontractors. Publishers of fleet management solutions must therefore ensure compliance with these regulations. Obviously, these constraints seem difficult. For publishers, Big Data is a vector of growth. It helps to create new opportunities, to open up to new sectors and therefore possible changes in the economic model. This possibly induces changes in the economic model. For example, a publisher can move from a licensing model to service distribution (Software as a Service - SaaS). This makes it easier for clients to manage their budget, but also their offers to end users.