Optimize your operations or projects by obtaining insightful telematics data

Big Data has permeated many industries, altering the way we work. Big Data enables IoT platforms to operate, connecting devices and revolutionizing machines. While the fleet management industry is not usually associated with the term "innovation," Big Data has changed that. The infrastructure of the Big Data Solution from AutoPi has specifically been designed for a high amount of telematics data from a lot of devices at once and to retain the data for a long period.

The Big Data Solution is built on a time series database, which gives advantages in hosting, as there is a potential for using external storage volumes, which are cheaper and therefore, needed for extended retention of huge databases. Our solution is customizable, meaning that we can tailor a businesss solution exactly to your project needs. In other words, you will be able to gain very large datasets that can be analyzed computationally to reveal patterns, trends, and associations – especially in connection with human behavior and interactions.

Big Data analytics uses analytic techniques to examine data, thus obtaining and finding out information like hidden patterns, correlations, market trends, and consumer preferences. Therefore, analytics help businesses and organizations make informed business decisions that lead to efficient operations, happy consumers, and increased profits.

Big Data analytics entails processing large amounts of data in order to discover patterns and/or assign values to the data. E.g. Data storytelling. Businesses can improve their models to better meet the needs of their customers. Data collection is pointless if it cannot be used to its full potential. Organizations that have yet to leverage big data must begin taking steps to implement big data technologies.

Telematics is built on the technology of collecting, storing and transmitting information between end-users and vehicles via telecommunication devices. Big data use cases in telematics, increase the data's usefulness. So, what kinds of valuable services can big data bring to the world of telematics:

• Optimization of Routing (use case #1): The number of possible routes for a truck is extremely large. Previously, logistics companies had to plan a delivery path manually, which took a long time. Furthermore, it was difficult to provide accurate ETAs and save driving time on the road without real-time traffic conditions.

  Big data relies on several key types of data to evaluate the best route based on consuming time and fuel usage in a few seconds to make the driving oath more efficient and optimal: Telematics tracking devices collect real-time GPS and speed data. Instant traffic information, such as accidents and construction zones reported by other road users; Authorized institutions post road information such as the number of stop signs, road speed, school zone, and so on. The optimized route is then calculated and displayed to the driver.

• Maintenance Reminders and Breakdown Alerts (use case #2): Engine data collected by telematics devices include engine RPM, oil level, transmission, mileage driven, tire pressure, and more. Big data predictive analysis could provide us with precautionary breakdown and maintenance notifications, as well as recommended solutions, based on all of the engine data and historical records of maintenance and repair. By gaining early insight into potential vehicle health issues, the fleet can balance downtime and work time for vehicles, reducing the likelihood of an unexpected breakdown on the road.

• Analysis of Driving Behavior (use case #3): People are often referred to as a company's most valuable resource. Driving and road safety has always been and will always be the top priorities in the transportation industry. Running a large global fleet makes managing safety even more difficult. As a result, analyzing driver behavior is critical for both fleet management and the drivers themselves.

  It is widely acknowledged that a better understanding of driving behavior aids in the development of more appropriate safety policies, more intelligent driving guiding systems or coaching systems, and, most importantly, in lowering the rate of accidents while protecting company property and reputation, as well as drivers' lives. More and more in-depth machine learning and artificial intelligence algorithms have been performed in recent years to investigate drivers' driving behaviors and styles. Telematics can detect speed-related, stop-related, turn-related, and other driving behaviors.