Processual Approach to Data Quality: The Case of ‘Task-Based Tuning’ In a Professional Organization

Abstract

We develop an empirically grounded understanding of how data quality emerges and changes over time in the practices of professionals working in an organization. This is important because literature on data quality focuses on how ‘good’ quality data informs managerial decision making. However, in professional organizations, somewhat independent professionals make key decisions based on their expertise. Our process model speaks to these issues, highlights the importance of data quality for digital innovation more generally, and informs literature on heterogenous professions in organizations.