The Use of Data Quality Information (DQI): An Exploratory Study

Helen-Tadesse Moges, Wilfried Lemahieu, Bart Baesens

Abstract


Prior research has indicated that the outcome of decision-making can be influenced by many factors. One of these factors is the quality of the data needed for the decision task. Data Quality (DQ) is increasingly becoming an issue as companies are collecting more and more complex data. Poor quality data is a key cause of poor decision quality. As a result, DQ has received a lot of attention, both by organizations worldwide and in academic literature. Despite recognizing the importance of improving DQ, organizational data are far from perfect. Since DQ is not always good, literature suggests communicating the DQ level in the form of DQ information (DQI) to users as one technique to minimize the detrimental effect of poor quality data on decision-making processes because DQI allows users to recognize and account for imperfections in their decisions. DQI is the set of quality measurements associated with the data. However, the process of creating, storing and maintaining DQI in databases is expensive and, thus, would need to be justified by a clear understanding of how DQI affects decision-making. Although some researchers revealed the use of DQI for decisions when experience level progresses and the complexity of task decreases, there is no consensus under which decision-making strategies the DQI impacted the decision-making processes. Therefore, this paper reports an experiment that examines the impact of DQI (DQ level information about data accuracy) on decision outcomes for two decision strategies: additive and elimination by attributes. The results of the statistical analysis showed no significant impact on decision choice or confidence. However, the consensus level is decreased with DQI for the additive strategy. The results of this study suggest that DQI included in databases may only be useful when certain decision-making strategies are included in query facilities. This knowledge will be essential for companies with diverse database systems, and designers of data warehouses and decision support systems.

Keywords: 

Data Quality, Data Tag, Decision Strategy, Decision Support Systems

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