Data quality in IoT and smart manufacturing environments is essential for optimizing workflows, enabling predictive maintenance, and supporting informed decisions. However, data from sensors present significant challenges due to their real-time nature, diversity of formats, and high susceptibility to faults such as missing values or inconsistencies. Ensuring high-quality data in these environments is crucial to maintaining operational efficiency and process reliability. This paper analyzes some of the data quality metrics presented in the literature, with a focus on adapting them to the context of Industry 4.0. Initially, three models for the classification of the dimensions of data quality are presented, proposed by different authors, which group together dimensions such as accuracy, completeness, consistency, and timeliness in different approaches. Next, a systematic methodology is adopted to evaluate the metrics related to these dimensions, always using a real-time monitoring scenario. This approach combines dynamic thresholds with historical data to assess the quality of incoming data streams and provide relevant insights. The analysis carried out not only facilitates continuous monitoring of data quality but also supports informed decision-making, helping to improve operational efficiency in Industry 4.0 environments. Finally, this paper presents a table summarizing the selected metrics, highlighting the advantages, disadvantages, and potential usage scenarios, and providing a practical basis for implementation in real environments.

In modern industrial environments, data-driven decision-making plays a crucial role in ensuring operational efficiency, predictive maintenance, and process optimization. However, the effectiveness of these decisions is highly dependent on the quality of the data. Industrial data is typically generated in real time by sensors integrated into IoT devices and smart manufacturing systems, resulting in high-volume, heterogeneous, and rapidly changing data streams. This paper presents the design and implementation of a data quality pipeline specifically adapted to such industrial contexts. The proposed pipeline includes modular components responsible for data ingestion, profiling, validation, and continuous monitoring, and is guided by a comprehensive set of data quality dimensions, including accuracy, completeness, consistency, and timeliness. For each dimension, appropriate metrics are applied, including accuracy measures based on dynamic intervals and validations based on consistency rules. To evaluate its effectiveness, we conducted a case study in a real manufacturing environment. By continuously monitoring data quality, problems can be proactively identified before they impact downstream processes, resulting in more reliable and timely decisions.