Attribute of Big Data Analytics Quality Affecting Business Performance

Abstract

With an accelerating increase of business benefits produced from big data analytics (if used appropriately and intelligently by businesses in the private and public sectors), this study focused on empirically identifying the big data analytics (BDA) attributes. These attributes were classified into four groups (i.e., value innovation, social impact, precision, and completeness of BDA quality) and were found to influence the decision-making performance and business performance outcomes. A structural equation modeling analysis using 382 responses from a BDA related to practitioners indicated that the attributes of representativeness, predictability, interpretability, and innovativeness as related to value innovation greatly enhanced the decision-making confidence and effectiveness of decision makers who make decisions using big data. In addition, individuality, collectivity, and willfulness, which are related to social impact, also greatly improved the decision-making confidence and effectiveness of the same decision makers. This shows that the value innovation and social impact, which have received relatively less attention in previous studies, are the crucial attributes for BDA quality as they influence the decision-making performance. Comprehensiveness, factuality, and realism, which are linked to completeness, also have similar results. Furthermore, the higher the decision-making confidence of the decision makers who used big data was, the higher the financial performance of their companies. In addition, high decision-making confidence using big data was found to improve the nonfinancial performance metrics such as customer satisfaction and quality levels as well as product development capabilities. High decision-making effectiveness with big data was also shown to improve the nonfinancial performance metrics.