Authors:
Teresa Peixoto
1
;
Bruno Oliveira
1
;
Óscar Oliveira
1
and
Fillipe Ribeiro
2
Affiliations:
1
CIICESI, School of Management and Technology, Porto Polytechnic, Portugal
;
2
JPM Industry, Portugal
Keyword(s):
Data Quality, Data Profiling, Real-Time Data Analysis, Smart Manufacturing Environments, Industry 4.0.
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 da
ta-driven decision-making processes in smart manufacturing environments.
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