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.