Work

Interrelations Among Data Quality Characteristics in Safety-Relevant Maritime Services: An Expert-Based Study

Year: 2026

Type: article

Abstract: The digitalization of maritime information and operational data has led to increasing reliance on data-driven services to support safe and efficient maritime transportation. As maritime operations become more service-oriented, data quality and interoperability play a crucial role in operational reliability and safety-related decision-making. However, most existing data quality assessment approaches treat quality characteristics as independent attributes, offering limited insight into how deficiencies interact and propagate risk across interconnected services. This study proposes a safety-oriented analytical framework that focuses on the interrelations among data quality characteristics in service-oriented maritime operations. The framework combines Delphi-inspired expert elicitation with a structured prioritization approach based on the principles of the Analytic Hierarchy Process to capture both the perceived importance of data quality characteristics and their influence relationships. An expert survey was conducted with practitioners across the maritime transportation value chain, focusing on safety-relevant services such as condition monitoring and route planning. The results indicate that data quality characteristics are perceived as interdependent rather than isolated. Timeliness is identified as a key upstream factor that strongly influences both accuracy and reliability, especially in operational services that operate under time constraints. The analysis further shows that operational services are more vulnerable to compounded data quality degradation than reporting-oriented services. By framing data quality interactions as latent safety and interoperability risk factors, this study provides a practical perspective that complements existing standards and supports more effective prioritization of data quality management in digital maritime services.

Source: Applied Sciences

DOI: 10.3390/app16042020

Authors: Changui Lee, Seojeong Lee

Institution: Korea Maritime and Ocean University

Language: English

Topic: Data Quality and Management

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896