Recent technological developments have facilitated the continuous identification and tracking of indi-vidual objects/ things moving in space. Business analytics tools can handle the resulting vast amount of object tracking data. Thus, tracking technologies could be viewed as information facilitators that can directly improve decision-making. This research suggests a data-driven approach that transforms the simple object movement events captured by tracking devices in a monitored area into objects’ flows composing a network. In addition, we devise two new metrics, the volume and the mobility of the moving objects per flow to characterize the objects movement patterns. The proposed approach offers a structured way to transform massive tracking data into valuable, new knowledge of the moving be-havior of objects that can support a wealth of business decisions. We demonstrate the utility of the proposed approach with real Radio Frequency Identification (RFID) data representing garments’ movements in a retail store of a fashion retailer.