Identifying and Prioritising Factors for Effective Decision-Making in Data-Driven Organisations: A DEMATEL Approach

Abstract

The strategic transformation of increasing data volumes into managerial decisions is critical for organisational performance and sustainability; yet, it faces hurdles like poor data quality, technological deficiencies, and skill gaps. This study investigates the causal interdependencies among key factors influencing data-driven decision-making within data-driven organisations. Utilising the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, a robust structural and Multi-Attribute Decision-Making (MADM) technique, expert judgments from five management-level professionals were analysed to construct direct and total-relation matrices. The results classify Data Analytics Literacy (DAL) and Business-Strategy Alignment (BSA) as primary causal factors, while Data Quality (DQ), Data Infrastructure & Technology (DIT), and Data Culture & Governance (DCG) emerge as effect factors. These findings provide a structured framework for prioritising managerial interventions, suggesting that strengthening foundational elements (DAL and BSA) will significantly enhance analytical capabilities and strategic alignment. A limitation is the small, expert-based sample, indicating the potential for future validation with larger, more diverse panels or Fuzzy-DEMATEL applications.