Browsing Articles by Subject "Data Quality Metadata (DQM)"
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Determining the use of data quality metadata (DQM) for decision making purposes and its impact on decision outcomes — An exploratory studyDecision making processes and their outcomes can be affected by a number of factors. Among them, the quality of the data is critical. Poor quality data cause poor decisions. Although this fact is widely known, data quality (DQ) is still a critical issue in organizations because of the huge data volumes available in their systems. Therefore, literature suggests that communicating the DQ level of a specific data set to decision makers in the form of DQ metadata (DQM) is essential. However, the presence of DQM may overload or demand cognitive resources beyond decision makers' capacities, which can adversely impact the decision outcomes. To address this issue, we have conducted an experiment to explore the impact of DQM on decision outcomes, to identify different groups of decision makers who benefit from DQM and to explore different factors which enhance or otherwise hinder the use of DQM. Findings of a statistical analysis suggest that the use of DQM can be enhanced by data quality training or education. Decision makers with a certain level of data quality awareness used DQM more to solve a decision task than those with no data quality awareness. Moreover, those with data quality awareness reached a higher decision accuracy. However, the efficiency of decision makers suffers when DQM is used. Our suggestion would be that DQM can have a positive impact on decision outcomes if it is associated with some characteristics of decision makers, such as a high data quality knowledge. However, the results do not confirm that DQM should be included in data warehouses as a general business practice, instead organizations should first investigate the use and impact of DQM in their setting before maintaining DQM in data warehouses.