Comprehensive Architecture for Data Quality Assessment in Industrial IoT

Abstract—The rapid growth of the Industrial Internet of
Things (IIoT) has led to the generation of vast amounts of
data, which has significant implications for decision-making in
various industrial sectors and is increasingly being traded on data
marketplaces. Quantifying IIoT data quality and providing measures
to improve, it is critical for both operational efficiency and
business value. This paper presents a comprehensive architecture
for data quality assessment in the IIoT, aimed at ensuring the
quality, trustworthiness, and reliability of the data generated by
the IIoT. The architecture facilitates both objective and subjective
assessments, taking into account the intended task for the data,
and includes data enhancement operations to address data quality
issues. The architecture provides a standardized and modular
approach to data quality evaluation, allowing data owners and
data market participants to make informed decisions about the
quality and value of their data. The proposed architecture has
the potential to significantly impact various industrial sectors
and data marketplaces, providing a valuable tool for ensuring
the reliability and accuracy of IIoT data. As the architecture is a
work in progress, the preliminary evaluation of its effectiveness
has been omitted for future work.