Data is abundant in the fleet industry and can be overwhelming. There is data flowing to a fleet manager like an open firehose from many sources and in varying formats. To make the data actionable a few things need to happen: automation, normalization, and aggregation.

Automate Data Collection

Fleet managers need to consume several different types of data to create a holistic view of the driver and accomplish their driver safety goals. When data collection is automated it is possible to collect from additional sources – the more sources of driver data that can be consumed, the clearer the view of the driver becomes. To accurately assess driver behavior and create a holistic view, multi-sourced, real-time data needs to be collected and automated. Examples of relevant data sources include continuous MVR monitoring, CSA scores, telematics, and third-party safety equipment vendor data.

Data Normalization

Data normalization is generally considered the development of clean data. Diving deeper, however, the meaning or goal of data normalization is twofold:

  1. Data normalization is the organization of data to appear similar across all records and fields.
  2. It increases the cohesion of entry types leading to cleansing, lead generation, segmentation, and higher quality data.

Simply put, this process includes eliminating unstructured data and redundancy to ensure logical data storage. When data normalization is done correctly, the result is a standardized information entry.

Aggregate Data into One Central System – Creating a Holistic View of the Driver

Each set of data that is collected from a particular source, whether it is historical or real-time, is just one piece of the picture of the driver’s behavior. To create the sought-after holistic view of the driver, data needs to be aggregated into one central system or platform. When the driver manager can see past behavior from the MVR and CSA scores and then combine historical data with newly posted violations and real-time critical event tracking, the data has become actionable. The driver manager can now use the data to improve driver behavior, mitigate risk and reduce operating costs.

Learn more about creating and using actionable data in “Using Data to Improve Driver Safety”