Standardising Data Quality in IoT-to-AI Workflows: A Formal Multilayered Architecture for Reliable and Quality-Assured Information Systems

This paper presents the Data Quality Assurance Model (DQAM), a formal model and multilayered architecture designed to guarantee data integrity and robustness in Reliable and Quality-Assured Information Systems. Recognising that inaccurate or corrupted sensor data can lead to system collapses and false alarms in critical services, the DQAM provides a standardised and systematic flow of actions to ensure data excellence for Artificial Intelligence (AI). The architecture is structured into three specialised layers (Acquisition, Processing, and AI Adequacy), implementing formal transformation functions that act as a rigorous filter against data degradation. A core contribution is the mapping of these functions to ISO/IEC 25012 and 5259-2 standards, providing a practical framework for reliable information management. It should be noted that quality dimensions regarding timeliness and data volume are outside the scope of this work, as they depend on external data issuers and end-service requirements. The model’s viability is validated through a real-world implementation on a university campus managing millions of data points, demonstrating its capability to optimise performance—achieving a speedup of up to 43%—and prevent service malfunctions. This work bridges the gap between raw IoT streams, and the high-integrity standards required by modern AI-driven applications.