telematics, dispatcher
Telematics data can be daunting without the discipline to manage it.

In their book, L’informatisation de la Societe, (translated, The Computerization of Society), Simon Nora and Alain Minc introduced a concept that married the practices of computer use with the science of telecommunications. Over the years, this concept of telematics, the English language version of Nora and Minc’s French word telematique, became ubiquitous and eventually took on a specifically vehicular context. Since its inception almost 40 years ago, telematics has evolved from a manual recording of information in computer databases to simple GPS tracking to modern-day, complex logistics systems outfitted with remote technology and data automation. Therein lies much of the challenge today’s fleet managers face when making telematics data work for their companies. Fleet managers may find themselves paralyzed when presented with the huge amounts of data provided by such applications. To get a handle on telematics data (and get the most out of their investment), fleet managers should consider these three crucial steps to make telematics work for their companies.

Configure telematics equipment and software correctly

Have you ever looked through telematics data, only to question its validity in the end? Something doesn’t seem quite right. Maybe there are anomalies or outliers – an MPG number is unusually higher or lower than others; spotty coverage leaves significant gaps in distances traveled for one vehicle; an isolated inventory level doesn’t align with your instincts based on the number of stops the truck made. Or on the other end of the “not-quite-right” spectrum, the data is just too perfect – MPG numbers are identical from top to bottom on your vehicle log; safety alerts are nonexistent for the last six months. This data would be fantastic if it were accurate, but your instincts likely tell you that they are just too good to be true.

Data integrity is tied directly to the proper configuration of data collection tools. Even the best telematics software cannot provide accurate results if not configured properly. To ensure the strength, accuracy, and value of telematics data, and to avoid nagging questions about data validity over time, telematics systems must be installed correctly and optimized, monitored, and updated as necessary.

Devise and revisit concrete goals

To get the most out of your telematics data, devise concrete goals. These goals will provide needed context for your data. Be intentional in determining your specific telematics data needs. Is your goal to lower fuel costs? Decrease vehicle wear and tear? Reduce traffic violations? Whatever your goals may be, articulate them clearly, and be prepared to revisit them when external influences such as increased fuel prices or new governmental regulations change the delivery landscape.

Aligning fleet management goals with larger company goals becomes simple when you include robust fleet management applications in the resource mix. Goal-centered data should be easily identifiable and able to be parsed into a variety of meaningful configurations to aid in resource justification. By matching hard data to shared goals, fleet managers can make ROI readily visible to company stakeholders.

Implement and test and implement and test…

While goal-setting is obviously critical to a solid fleet management strategy, without execution, goals are useless. One European study conducted in 2017 revealed that on a five-point scale, 30% of fleet managers surveyed said they used data provided by technological systems to inform decision making sometimes to never. That’s nearly a third of the participants who simply rejected in part or in full the data provided by systems that likely cost thousands in hardware, software, and maintenance. An effective fleet management strategy consistently includes concrete, verifiable, data-based implementation steps – action items that are guided by consequential data-points and key performance indicators (KPIs).

Once executed, each telematics implementation should include model assessment measures, such as a Kaggle competition that creates a telematic “fingerprint” of drivers to predict or describe telematics data. Vetted data speak to the strength (or weakness) of a telematics implementation. Informed by previously determined KPIs, a model assessment allows a fleet manager to optimize successive deployments, continually increasing efficiency and reducing waste.

From dispatch to delivery, telematics data can guide fleet managers to a profitable end. Proper configuration, concrete goals, and wise implementation and testing are essential in a practical, data-driven telematics strategy, and they help fleet managers get the most out of this important investment.

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