Abstract

This thesis explores the hidden costs of poor data quality in multinational organizations and proposes a practical model for estimating these costs. While prior research has addressed the technical dimensions of data quality, less attention has been given to the operational inefficiencies, time losses, strategic setbacks that often go unmeasured. The study adopts a pragmatic research philosophy and uses a qualitative, exploratory design to address this gap.

Primary data was collected through nine semi-structured interviews with data management and governance professionals, while secondary data was drawn from academic frameworks (including TDABC and COLDQ) and industry reports. Qualitative content analysis was applied to identify key cost drivers such as rework, hidden time loss, opportunity costs, governance gaps, and organizational fragmentation. These findings were triangulated with secondary sources to develop and refine the Hybrid Data Quality Cost Model (H-DQCM), a scenario-based tool designed to help organizations estimate hidden costs even in decentralized or fragmented environments.

The study concludes that poor data quality imposes significant but underestimated burdens on organizations and that even rough cost estimation can improve cost visibility, governance effectiveness, and strategic decision-making. The findings offer both practical recommendations for managers and theoretical contributions to the field of data quality management.