A Practical Data Quality Assessment Method for Raw Data in Vessel Operations

Abstract

With the current revolution in Shipping 4.0, a tremendous amount of data is accumulated during vessel operations. Data quality (DQ) is becoming more and more important for the further digitalization and effective decision-making in shipping industry. In this study, a practical DQ assessment method for raw data in vessel operations is proposed. In this method, specific data categories and data dimensions are developed based on engineering practice and existing literature. Concrete validation rules are then formed, which can be used to properly divide raw datasets. Afterwards, a scoring method is used for the assessment of the data quality. Three levels, namely good, warning and alarm, are adopted to reflect the final data quality. The root causes of bad data quality could be revealed once the internal dependency among rules has been built, which will facilitate the further improvement of DQ in practice. A case study based on the datasets from a Danish shipping company is conducted, where the DQ variation is monitored, assessed and compared. The results indicate that the proposed method is effective to help shipping industry improve the quality of raw data in practice. This innovation research can facilitate shipping industry to set a solid foundation at the early stage of their digitalization journeys.

Keywords

Data quality / Vessel operations / Shipping / Validation rules / Noon reports