Decision support systems

Decision-making processes are significantly influenced by a myriad of factors, with data quality emerging as a crucial determinant. Despite widespread awareness of the detrimental effects of poor-quality data on decisions, organizations struggle with persistent challenges because of the sheer volume of data within their systems. Existing literature advocates for providing Data Quality Metadata (DQM) to help decision-makers communicate data quality levels. However, concerns about potential cognitive overload induced by DQM may hinder decision-makers and negatively impact outcomes. To address this concern, we conducted an experimental exploration into the impact of Data Quality Management (DQM) on decision outcomes. Our study aimed to identify specific groups of decision-makers benefiting from DQM and uncover factors influencing its usage. Statistical analyses revealed that decision-makers with a heightened awareness of data quality demonstrated improved Data Quality Management (DQM) utilization, leading to increased decision accuracy. Nevertheless, a trade-off was observed as the efficiency of decision-makers suffered when employing Decision Quality Management (DQM). We propose that the positive impact of incorporating Data Quality Management (DQM) on decision outcomes is contingent on characteristics such as a high level of knowledge about data quality. However, we acknowledge that the inference of this positive impact could be more transparent and thoroughly explained. Our findings caution against a blanket inclusion of Data Quality Management (DQM) in data warehouses, emphasizing the need for tailored investigations into its utility and impact within specific organizational settings.