Overcoming Data Bias: Practical Challenges and Purification Approaches of Big Data in Management Decision-Making

Abstract

The strong connection between big data and artificial intelligence tools is changing the management decision-making paradigm. The article examines the issue of data bias in the involvement of big data in management decision-making. It suggests that data-driven decision-making would improve the accuracy of decision-making, efficiency in operations, and competitive advantage, as it would facilitate a proactive adaptation to the complex environment, provide a breakthrough in the internal management bottlenecks of the organization, and how to promote sustainable developmental objectives. Meanwhile, this paradigm has a deep endogenous problem, systemic data bias. The article indicates that the challenging issue of bias is not what can be technically fixed, but instead, a strategic requirement of responsible innovation. It presupposes the transition to a different decision-making paradigm, alongside efficiency-based automation, to a novel decision-making approach based on intelligence and collaboration between people and machines. Technological capabilities in the process should be matched with a level of ethical enquiry, organizational learning, and a desire to focus on the fairness of the outcome, which will, in an actual sense, help unlock the sustainable value of data in the age of intelligence.