Decoding DQM for Experimental Insights on Data Quality Metadata’s Impact on Decision-Making Process Efficacy

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 grapple with persistent challenges due to the sheer volume of data within their systems. Existing literature advocates for providing Data Quality Metadata (DQM) to aid decision-makers in communicating data quality levels. However, concerns about potential cognitive overload induced by DQM may hinder decision-makers and impact outcomes negatively. To address this concern, we conducted an experimental exploration into the impact of 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 an elevated awareness of data quality exhibited enhanced DQM utilization, resulting in higher decision accuracy. Nevertheless, a trade-off was observed as the efficiency of decision-makers suffered when employing DQM. We propose that the positive impact of incorporating DQM on decision outcomes is contingent on characteristics such as a high level of data quality knowledge. 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 DQM in data warehouses, emphasizing the need for tailored investigations into its utility and impact within specific organizational settings.