Managing Product Quality in a Multi-Use-Case Master Data Management Environment : A case study in the pharmaceutical industry
Huntus, Isla (2026)
2026
Tietojohtamisen DI-ohjelma - Master's Programme in Information and Knowledge Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2026-06-12
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202606107232
https://urn.fi/URN:NBN:fi:tuni-202606107232
Tiivistelmä
Organizations increasingly rely on high-quality master data to support operational processes, regulatory compliance and integrated information systems. In the pharmaceutical industry, product master data plays a particularly critical role because it is used across multiple business functions, regulatory processes and downstream systems. Managing product data quality becomes especially challenging in multi-use-case Master Data Management (MDM) environments, where different business domains and regulatory contexts impose varying requirements for data completeness, consistency and validity. The objective of this study was to examine how product data quality is created and managed within a complex pharmaceutical MDM environment and to identify opportunities for improving data quality management through governance, system controls and performance measurement.
The theoretical framework combines literature on Master Data Management, data quality, data governance and pharmaceutical regulatory requirements. Particular attention is given to the relationship between system configurations, governance mechanisms and human factors in shaping data quality outcomes. The study adopts a qualitative single-case study approach. Empirical data were collected through semi-structured interviews with stakeholders involved in product data creation, maintenance and governance, complemented by internal documentation and process materials. The data were analysed using thematic analysis and interpreted through an abductive research approach.
The findings show that product data quality is not determined by individual technical controls but emerges through the interaction of system configurations, governance structures and organizational practices. The results indicate that centralized master data maintenance and specialized data management roles support consistent data handling practices, while variability in data quality outcomes is influenced by differences in interpretation, use-case requirements and the balance between flexibility and control. The study also identified gaps between documented requirements and system-level enforcement, highlighting the importance of aligning governance practices, documentation and technical controls. Furthermore, the findings suggest that visibility into data quality performance could be improved through more systematic measurement and monitoring.
Based on the findings, the study proposes a practical framework for data quality management centred on key performance indicators (KPIs) related to completeness, consistency, validity and timeliness. The recommendations emphasize a balanced and risk-based approach, combining selective system enforcement, governance-driven improvement activities and continuous monitoring. The study contributes to existing literature by providing an empirical perspective on how product data quality is managed in a regulated, multi-use-case MDM environment and by illustrating how data quality emerges through the interaction of technical, organizational and governance-related factors.
The theoretical framework combines literature on Master Data Management, data quality, data governance and pharmaceutical regulatory requirements. Particular attention is given to the relationship between system configurations, governance mechanisms and human factors in shaping data quality outcomes. The study adopts a qualitative single-case study approach. Empirical data were collected through semi-structured interviews with stakeholders involved in product data creation, maintenance and governance, complemented by internal documentation and process materials. The data were analysed using thematic analysis and interpreted through an abductive research approach.
The findings show that product data quality is not determined by individual technical controls but emerges through the interaction of system configurations, governance structures and organizational practices. The results indicate that centralized master data maintenance and specialized data management roles support consistent data handling practices, while variability in data quality outcomes is influenced by differences in interpretation, use-case requirements and the balance between flexibility and control. The study also identified gaps between documented requirements and system-level enforcement, highlighting the importance of aligning governance practices, documentation and technical controls. Furthermore, the findings suggest that visibility into data quality performance could be improved through more systematic measurement and monitoring.
Based on the findings, the study proposes a practical framework for data quality management centred on key performance indicators (KPIs) related to completeness, consistency, validity and timeliness. The recommendations emphasize a balanced and risk-based approach, combining selective system enforcement, governance-driven improvement activities and continuous monitoring. The study contributes to existing literature by providing an empirical perspective on how product data quality is managed in a regulated, multi-use-case MDM environment and by illustrating how data quality emerges through the interaction of technical, organizational and governance-related factors.