Quality 4.0: Data Quality and Integrity: A Computational Approach

Abstract

The use of modern techniques, such as IOT, AI, and machine learning, revolutionized the idea of quality and quality control. Auditors face a tidal wave of data. One of the key challenges is how to determine the quality of the data, systems and processes produce. We propose a computational model to learn the inherent uncertainty to data integrity subsumed in the claims actually done by stakeholders within and outside the organization. The decision procedure combines two strong forms of obtaining audit evidence. These two forms are external conformation and re-performance. The procedure fits in the current modern computational idea data-driven assurance, which is consistent with quality 4.0 concepts in quality control and quality audit practices.