Real-Time Manufacturing Data Quality: Leveraging Data Profiling and Quality Metrics

Teresa Peixoto, Bruno Oliveira, Óscar Oliveira, Fillipe Ribeiro

2025

Abstract

Ensuring data quality in decision-making is essential, as it directly impacts the reliability of insights and business decisions based on data. Data quality measuring can be resource-intensive, and it is challenging to balance high data quality and operational costs. Data profiling is a fundamental step in ensuring data quality, as it involves thoroughly analyzing data to understand its structure, content, and quality. Data profiling enables teams to assess the state of their data at an early stage, uncovering patterns, anomalies, and inconsistencies that might otherwise go unnoticed. In this paper, we analyze data quality metrics within Industry 4.0 environments, emphasizing various critical aspects of data quality, including accuracy, completeness, consistency, and timeliness, and showing how typical data profiling outputs can be leveraged to monitor and improve data quality. Through a case study, we validate the feasibility of our approach and highlight its potential to improve data-driven decision-making processes in smart manufacturing environments.

Paper Citation

in Harvard Style

Peixoto T., Oliveira B., Oliveira Ó. and Ribeiro F. (2025). Real-Time Manufacturing Data Quality: Leveraging Data Profiling and Quality Metrics. In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS; ISBN 978-989-758-750-4, SciTePress, pages 56-68. DOI: 10.5220/0013242900003944

in Bibtex Style

@conference{iotbds25,
author={Teresa Peixoto and Bruno Oliveira and Óscar Oliveira and Fillipe Ribeiro},
title={Real-Time Manufacturing Data Quality: Leveraging Data Profiling and Quality Metrics},
booktitle={Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
year={2025},
pages={56-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013242900003944},
isbn={978-989-758-750-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - Real-Time Manufacturing Data Quality: Leveraging Data Profiling and Quality Metrics
SN - 978-989-758-750-4
AU - Peixoto T.
AU - Oliveira B.
AU - Oliveira Ó.
AU - Ribeiro F.
PY - 2025
SP - 56
EP - 68
DO - 10.5220/0013242900003944
PB - SciTePress