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In 1978, Simon Nora and Alain Minc coined a term, the reach of which they likely never predicted. In their book, L’informatisation de la Societe, (translated, The Computerization of Society), Nora and Minc introduced a concept that married the practices of computer use with the science of telecommunications1. Over the years, this concept of telematics, the English language version of Nora and Minc’s French word telematique, has become ubiquitous, and over time, the term has taken on a specifically vehicular context. Since telematics was christened as such almost 40 years ago, it has evolved from a manual recording of information in computer databases to simple GPS tracking, to modern-day, complex navigation systems outfitted with remote technology and data automation. Therein lies much of the challenge modern-day fleet managers are faced with in making telematics data work for their companies.

While the maturation of telematics has enabled businesses save thousands of dollars each year, the volume of data now available through telematics applications can be dizzying. 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 to get the most out of their investment), fleet managers should consider these three important steps in making telematics work for their companies.

Make sure that telematics equipment and software are configured correctly

Have you ever perused a lengthy compilation of otherwise beautiful data, only to question its validity in the end? Something just doesn’t seem quite right. Maybe there are anomalies, or on the other end of the “not-quite-right” spectrum, the data is too perfect.

Data integrity is tied directly to the proper configuration of data collection tools. Even the best telematics software cannot provide accurate results if it is 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

In order to wring the most worth from your telematics data, devise concrete goals. These goals will provide a needed context for your data. Be intentional in determining your specific telematics data needs. Is your goal the lowering of fuel costs? The decrease of vehicle wear and tear? The reduction of traffic violations? Whatever your goal(s) 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.

The alignment of fleet management goals with larger company goals becomes simple when robust fleet management applications are included in the resource mix. Goal-centered data should be easily-identifiable and should be 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 absolutely critical to a solid fleet management strategy, without execution, goals are useless. An effective fleet management strategy includes concrete, verifiable 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 – data that speak to the strength of said implementation. Informed by previously determined KPIs, a judicious model assessment allows a fleet manager to optimize successive implementations, 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.

1 – http://www.globaltelematics.com/telematics.htm


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