Data Governance Under Constraints: How Frontline Bank Employees Maintain Customer Data Retention

Abstract

In the era of digital banking, customer data has become one of the most strategic assets of financial institutions, and the quality of its maintenance plays a critical role in decision making, risk management, and regulatory compliance. Nevertheless, frontline bank employees often operate under conditions shaped by infrastructural limitations, time pressure, complex regulations, and multiple operational requirements. This study aims to explain how frontline employees maintain the quality of customer data under such constraints and to examine the role of data governance in this process. Adopting a qualitative and exploratory case study approach, data were collected through semi structured interviews, participant observation, and a review of internal organizational documents. Thematic analysis was used to analyze the data. The findings reveal that employees face a combination of technical, organizational, and environmental constraints that can compromise the accuracy and completeness of data. However, factors such as targeted training, managerial support, process standardization, and the development of a data driven mindset among employees strengthen their ability to preserve data quality. The results indicate that frontline employees’ data practices function as a key mediating variable between data governance mechanisms and the final quality of customer information. According to the study’s final model, data quality emerges from the interaction between governance structures, operational conditions, and employee behavior. Addressing structural and human dimensions simultaneously can enhance the effectiveness of data governance within banking operations. Beyond contributing to the literature by emphasizing the human factor in data governance, this research offers practical recommendations for improving data management in banks.

Keywords: data governance, data maintenance, frontline bank employees, organizational constraints, data quality, customer data management, operational challenges, digital banking