Identifying and Prioritizing Factors for Effective Data-Driven Decision-Making in Organizations: A DEMATEL Approach

The increasing volume of data necessitates its effective transformation into managerial decisions for organizational performance and sustainability within Business Process Management. However, challenges like poor data quality, technological deficiencies, cultural resistance, and skill gaps often hinder this crucial process; thus, a profound understanding of the causal relationships among influencing factors is essential to address these impediments. This study utilizes the Decision-Making Trial and Evaluation Laboratory method, a robust structural and multi-criteria analysis technique, to analyze complex causal interdependencies. DEMATEL quantifies expert judgments and maps cause-and-effect relationships, offering a systemic perspective distinct from other Multi-Attribute Decision-Making methods. We examine five key factors: data quality, data infrastructure & technology, data culture & governance, data analytics literacy, and business-strategy alignment. Expert data from five management-level professionals were used to construct direct and total-relation matrices, deriving influence and causality scores. The DEMATEL application will provide comprehensive factor interdependencies via direct and total-relation matrices. D-R indicators will classify cause and effect factors, culminating in a causal map illustrating the system's structure and each factor's role. This study aims to offer a structured framework for effective data-driven culture and business process optimization.