The impact of data quality and analytical capabilities on planning performance: insights from the automotive industry

Conventional wisdom suggests that data quality plays a central role for compiling valid and reliable plans to make the right decisions. At the same time, it is acknowledged that planning processes are both data and knowledge intensive and characterized by the human-computer interface. However, there are limited academic investigations on how data quality and analytical capabilities simultaneously impact planning performance. Drawing on the conceptual approach of business analytics, we introduce the notion of analytical capabilities, which is operationalized through three distinct resources: ITusability, user competence, and analytical execution. To assess the impact of data quality and analytical capabilities on planning performance, we develop a structural equation model, which is then tested using data from the automotive industry. Our results suggest that analytical capabilities are a significant mediator for the effect of data quality on planning performance.