The Effect of Data Quality on Decision-Making. A Quasi Experimental Study

Abstract

The current study aimed to investigate the relationship between data quality dimensions (completeness and timeliness) and decision-making efficiency. The researcher adopted the quasi-experimental approach to answer the research questions and hypotheses. The study participants consist of 60 subjects, distributed into 2 groups. The first group consisted of 20 sales executives from Saudi beverage manufacturing companies. The other group had 40 participants from Al-Imam university. The study experiment consisted of 4 scenarios, two for each dimension, that give the participants scenarios and ask them to make the best decision based on these data. The scenarios were applied through face-to-face meeting and time to take decisions was recorded by participants. For the first data quality dimension, completeness of data, both groups got scenarios where complete and incomplete data were offered, and they were asked to choose the best available option based on the offered data. For the second dimension, the groups were offered up-to-date data and obsolete data and were asked to choose the best decisions based on the scenarios. The results were analyzed for correlations to check If there is a correlation between the responses of the two groups. The study found strong evidence for a correlational relationship between data quality dimensions and decision-making efficiency at 0.05 and 0.01 significance level for the student and employee groups respectively. The study found that high-quality data leads to both better and faster decisions in both groups. There were no significant differences between occupation, or gender and time taken for making a decision. The current study highlights the importance of data quality dimensions. It urges organizations to use up-to-date data, and complete data sets to base their decisions.