Data Quality Assessment Methodology
Daniela Delinschi, Rudolf Erdei, Emil Pasca, Oliviu Matei

Abstract. High-quality data is a precondition for reliable machine learning, analytics and decision support. This paper introduces a methodology for systematic data quality assessment that combines dimensions such as completeness, consistency, accuracy, timeliness and validity into a unified evaluation framework. The methodology defines reusable measurement procedures, scoring schemes and aggregation rules that can be applied at the level of individual fields, datasets or entire data pipelines. By providing a structured way to detect and quantify data quality issues, the proposed approach supports continuous monitoring, helps prioritise remediation actions and improves the trustworthiness of downstream data-driven services, with a particular focus on smart agriculture data ecosystems.

Keywords: data quality; data quality dimensions; assessment methodology; data governance; smart agriculture

Daniela Delinschi, Rudolf Erdei, Emil Pasca, Oliviu Matei, "Data Quality Assessment Methodology", Proc. 19th SOCO Int. Conf. on Soft Computing Models in Industrial and Environmental Applications, Springer, 2024, 2023.

Reference: Proc. 19th SOCO Int. Conf. on Soft Computing Models in Industrial and Environmental Applications, Springer, 2024.

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