Improving data quality: a case study of data governance in a testing organization - Keränen, Annu (2025)
The significance of data quality has increased with advancements in information systems and emerging technologies. High-quality data is essential for effective business operations and informed decision-making. Data quality is defined by its suitability for its intended purpose, making it context-dependent. As data sources and volumes grow, managing data becomes more critical. Structured data governance has been shown to mitigate data quality issues arising from poor data management. The goal of data governance is to ensure data quality, enable efficient data use, ensure compliance, reduce risks, and maximize data value. This study aimed to examine how data governance impacts data quality, identify key components of a data governance framework, and explore the organizational needs driving its implementation. The research combined a literature review on data quality and governance with empirical research through a case study in a software testing organization. Data were collected through semi-structured interviews and analyzed using thematic analysis with a hybrid deductive-inductive approach. The literature review highlighted the importance of structured governance efforts in improving data quality. Based on these insights, a data governance framework focused on data quality was developed and presented to interviewees for feedback. The empirical findings showed that while data governance is recognized as important, its practical implementation is lacking. Effective policies, clear responsibilities, and strong monitoring mechanisms were identified as essential for ensuring high-quality data. This study contributes to existing research by reinforcing the link between structured data governance and improved data quality, and highlighting the need for adaptable governance structures tailored to organizational needs.