Quantum traffic engineering is a revolutionary approach envisioned to give optimum solutions to network-related difficulties in quantum networks such as entanglement routing, resource management and so on. However, the crucial input to these strategies is traffic matrix. The traffic matrices (Fidelity and channel capacity matrices) provide the knowledge about the network which enables network engineers to apply reliable and resilient solutions to optimize the performance of quantum networks. There are extensive works of traffic matrix estimation in classical networks, however there is no work in literature that explain how to construct traffic matrices in quantum networks. In this work, we provide comprehensive step-by-step guidance for creating quantum traffic matrices. Moreover, we examine the effects of data analysis on traffic matrices that have been developed. Based on our findings, we observe that the fidelity matrix is significantly impacted by the methods used to execute quantum state reconstruction during data analysis, while the capacity matrix is significantly impacted by the likelihood that channels will be successfully distinguished.