Building a Data-Driven Culture A Case Study in the Airport Industry

Abstract

More and more firms are adopting data-driven practices, where decisions are made efficiently and with high accuracy by relying on data. This requires a data-driven culture, that supports and encourages the use of data for decision making. This master’s thesis explores the success factors and challenges in implementing a data-driven culture within a firm with heterogeneous operations, focusing on the Swedish airport operator Swedavia. Through a case study, the research compares barriers and enablers for a data-driven culture from theory with identified factors at the case company. The factors are presented in seven areas: management & leadership, resistance to change, vision & strategy, skills & competence, data management & technology, culture & communication, and results measuring & incentives. The result is that the challenges and success factors found at the firm aligns with factors found in theory to a high degree. Some prominent challenges that were found at the case company include lack of a tangible data strategy, lack of data literacy, and missing data governance practices. Opportunities that were identified include top management commitment, a fact- based mindset, and data democratization. Some factors were identified at Swedavia that has not been discussed in previous research. For instance, Swedavia has success factors related to collaboration with other airports and multi-competence among employees, which are not covered in existing literature. Conversely, barriers such as inter-functional communication issues, limited access to computers for operational level employees, and a perception of older coworkers being reluctant to adapt to new practices present challenges that are also less discussed in theory. In addition to contributing with empirical depth to existing literature, this thesis provides actionable recommendations for building a data-driven culture at Swedavia and similar organizations. These recommendations include ensuring clear commitment from both top and middle management, formulating and effectively communicating a data strategy and vision, building robust data governance practices, and promoting cross-functional collaboration.