Ship operational data are crucial for assessing vessel performance, predicting energy efficiency, and enabling predictive maintenance, thereby facilitating safe, efficient, and intelligent operations in the maritime industry. However, the effective utilization of such data is often hindered by poor data quality, high uncertainty, and complex processing workflows. To address these challenges, this paper proposes a robust framework for analyzing ship operational data. The framework enhances data usability by optimizing the procedures for data processing and analysis. Specifically, it incorporates a practical method for identifying steady-state operating conditions, establishes rational data filtering strategies, and applies internationally standardized methods for correcting environmental influences. The proposed framework is validated using real operational data from a container ship. Results demonstrate that the processed data can clearly reveal the vessel's long-term performance trends, providing a reliable basis for predictive maintenance decisions. This study offers a practical and effective solution for improving the quality and usability of ship operational data, thereby supporting the advancement of big-data applications towards smart shipping.