Can big data improve firm decision quality? The role of data quality and data diagnosticity

Abstract

Abstract Anecdotal evidence suggests that, despite the large variety of data, the huge volume of generated data, and the fast velocity of obtaining data (i.e., big data), quality of big data is far from perfect. Therefore, many firms defer collecting and integrating big data as they have concerns regarding the impact of utilizing big data on data diagnosticity (i.e., retrieval of valuable information from data) and firm decision making quality. In this study, we use the Organizational Learning Theory and Wang and Strong s data quality framework to explore the impact of processing big data on firm decision quality and the mediating role of data quality (DQ) and data diagnosticity on this relationship. We validate the proposed research model using survey data from 130 firms, obtained from data analysts and IT managers. Results confirm the critical role of DQ in increasing data diagnosticity and improving firm decision quality when processing big data; suggesting important implications for practice and theory. Findings also reveal that while big data utilization positively impacts contextual DQ, accessibility DQ, and representational DQ, interestingly, it negatively impacts intrinsic DQ. Furthermore, findings show that while intrinsic DQ, contextual DQ, and representational DQ significantly increase data diagnosticity, accessibility DQ does not influence it. Most importantly, the findings show that big data utilization does not significantly impact the quality of firm decisions and it is fully mediated through DQ and data diagnosticity. The results of this study contribute to practice by providing important guidelines for managers to improve firm decision quality through the use of big data.