Determining the use of data quality metadata (DQM) for decision making purposes and its impact on decision outcomes — An exploratory study

Abstract

Decision 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 causes 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 datawarehouses as a general business practice, instead organizations should first investigate the use and impact of DQM in their setting before maintaining DQM in datawarehouses.

Energy Internet: The business perspective

“…To discovery valuable knowledge and fully realize the business potential of energy big data, various big data analytics techniques, such as data quality evaluation and modeling [103][104][105], data clustering and classification [68,[106][107][108][109], stream data processing [110][111][112], knowledge inference [113,114], statistical machine learning [115], neural networks modeling and deep learning [116,117], can be implemented on the data. The objective of energy big data analytics is to develop more effective and efficient data-driven applications and services.…”

A Study of Accountability in Two Organizational Learning Frameworks: Why Accountability for Learning is Critical

“…Systems perceived as not being easy to use are unlikely to deliver the benefits anticipated, so this is a key factor to ascertain. Content Quality relates to the perceived inherent quality of the existing information within each system, chosen because content quality is critical to learning (Nelson et al, 2005;Haas and Hansen, 2007;Moges et al, 2016;Yen et al, 2015). Training refers to the availability of and experience with formal training for each system, as a critical feature of organizational learning (Davis and Davis, 1990;Gallivan et al, 2005;Sharma and Yetton, 2007;Lorenzo et al, 2009;Camps and Luna-Arocas, 2012;Sykes, 2015).…”

Quality of Information as an indicator of Trust in the Internet of Things

“…There is no agreement on a standard definition of DQ that can be applied across all data domains [15]. The intended use is commonly described as a multi-dimensional concept consisting of a set of quality attributes, called DQ dimensions which are determined by the data users [16,17]. In this study, it is assumed that information to be of high quality when they are "fit for use by data consumers", and they end up by selecting 15 different dimensions and grouped them under four different categories such as Intrinsic, Accessibility, Contextual, and Representational as depicted in Table 1.…”