Garbage In, Garbage Out: The Art in Managing Data Quality. Using KPIs to monitor and manage data quality in food and beverage retail.

In today’s digitally driven society, data is essential to decision-making and improving an organisation’s efficiency. Therefore, the aim for high data quality is a necessity for organisations. This study explores how to determine and monitor data quality through key performance indicators formulations, further aiming to bridge the gap in understanding the value of data quality, within an organisation that frequently uses data. The research questions for investigating and bridging the gap are as follows: “How can data quality be monitored within sustainable information management, particularly in organisations handling large volumes of article data?” and “What are potential Key Performance Indicators (KPIs) that retail companies in the food and beverage sector can use to assess data quality in article management?”. To investigate these research questions, a qualitative approach was taken, interviewing six (6) relevant roles that all work with data and related processes, also using a case for practical insights. Additionally, document analysis has been applied. The results present seven (7) categories derived through thematic analysis: Technology, External Environment, Human Aspects, Cooperation, Article Properties, Work Methods/-Processes, and Organisation. These categories emphasise that achieving high data quality requires an alignment between technical systems and social structures within the organisation, and in relation to external parties. This, supported by sustainable governance, communication, and role clarity. The Deposit Change Case exemplifies this interplay in practice. The discussion highlights the value of Key Performance Indicator-based monitoring, proposing indicators such as update frequency, error rates and correction lead times. This to assess critical dimensions of article data quality. The findings underline that data quality is both a technical and organisational concern, to align socio-technical aspects and contribute to sustainable information management. Future research could apply quantitative methods or longitudinal qualitative studies to explore the broader relevance and long-term impact of data management strategies in high-volume retail contexts.