Organizations understand the potential for turning both historical and streaming data into insights, and they have made huge investments in collecting, storing and analyzing telematics data. Unfortunately, mainstream CPU-based hardware and analytics solutions are too slow and difficult to scale. Despite large investments, far too much valuable telematics data is deleted, pre-aggregated or downsampled before analysis— most of the detail is lost. This leads to missed insights and incorrect conclusions.
The insights from historical analysis on telematics data are not time-sensitive for the logistics leader, but if analytic tools are too slow, that type of historical data exploration becomes too expensive and the analysis never gets done. In this guide, you’ll learn how to discover insights for strategic decisions on how to reduce fuel consumption, improve operator safety, reduce maintenance costs, optimize vehicle utilization, and predictably deliver according to schedule.