Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach

To make informed decisions, managers establish data warehouses that integrate multiple data sources. However, the outcomes of the data warehouse-based decisions are not always satisfactory due to low data quality. Although many studies focused on data quality management, little effort has been made to explore effective data quality control strategies for the data warehouse. In this study, we propose a chance-constrained programming model that determines the optimal strategy for allocating the control resources to mitigate the data quality problems of the data warehouse. We develop a modified Artificial Bee Colony algorithm to solve the model. Our work contributes to the literature on evaluation of data quality problem propagation in data integration process and data quality control on the data sources that make up the data warehouse. We use a data warehouse in the healthcare organization to illustrate the model and the effectiveness of the algorithm.