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This paper presents a novel data quality score designed to address the challenges of ensuring high-quality data in Internet of Things (IoT) deployments. Given the growing reliance on IoT systems and the volume of data they generate, maintaining data quality is essential for reliable decision-making and effective analytics. The proposed score synthesizes key data quality dimensions, providing a comprehensive measure of data quality that can be applied across various IoT contexts. The results obtained for a public dataset on a water pumping system show the applicability and flexibility of the proposed data quality score. This work contributes to the ongoing efforts to improve data management in IoT environments, ultimately supporting the development of robust, data-driven solutions.

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Data Quality Assessment: A Practical Application

  • 会议论文

  • 发表日期：

    2025年

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  • Eliana Costa e Silva,
  • Teresa Peixoto,
  • Óscar Oliveira,
  • Bruno Oliveira

摘要

This paper presents a novel data quality score designed to address the challenges of ensuring high-quality data in Internet of Things (IoT) deployments. Given the growing reliance on IoT systems and the volume of data they generate, maintaining data quality is essential for reliable decision-making and effective analytics. The proposed score synthesizes key data quality dimensions, providing a comprehensive measure of data quality that can be applied across various IoT contexts. The results obtained for a public dataset on a water pumping system show the applicability and flexibility of the proposed data quality score. This work contributes to the ongoing efforts to improve data management in IoT environments, ultimately supporting the development of robust, data-driven solutions.