The rapid growth of software industry has led to a significant increase in the production of a variety of data during software development process, highlighting the apparent need for improved data quality management. As an effective means of software process research and practice, Software Process Simulation Modeling (SPSM) requires large amount and high quality data that precisely depicts what happens during the development process. Accordingly, process simulation models can be used as a reference framework for assessing the issues in data management and data governance from a process perspective. The objective of the work reported in this paper is to provide insights into the data issues in real-world industrial settings and the corresponding coping strategies for software process modelers in particular in order to assist them in preparing and assessing data for their simulation models when conducting effective SPSM in the real-world settings. This paper reports on an empirical investigation that applies software process simulation practices to study the data issues and the data governance strategies based on an industrial case from one global ICT enterprise. As the outcome, a refined process for data preparation is presented, along with a taxonomy of the data issues and the corresponding coping strategies. This paper also explores traceability recovery approaches to mine more accurate process state information from software artifacts and analyzes the impact of the recovered data traceability information by evaluating the improved fidelity of the process simulation model.