Effective Data Quality Assurance Integrating Data Modelling and Governance

The increasing complexity and volume of data in the retail sector necessitate robust mechanisms for ensuring data quality and governance. This paper presents a MATLAB-based analytical framework designed to quantitatively assess and visualize critical data quality dimensions—including accuracy, completeness, consistency, and timeliness—alongside data governance parameters such as accessibility, integrity, security, and accountability. The proposed system leverages supervised machine learning models, namely Decision Trees, Random Forests, and Support Vector Machines (SVM), for classification, anomaly detection, and predictive analytics on simulated supply chain datasets. Feature engineering and dimensionality reduction techniques are applied to enhance model performance. The integration of machine learning with statistical quality control enables automated identification of data inconsistencies and governance lapses. Custom MATLAB scripts generate multi-dimensional plots, heat maps, and time-series graphs to depict data relationships and deviation trends. Experimental results demonstrate that implementing intelligent data governance frameworks improves data quality metrics, reduces system-level errors, and enhances business intelligence capabilities. The approach underscores the synergistic role of machine learning and governance in elevating data reliability and operational efficiency in retail systems.