Data-Driven Decision-Making : A proposed framework for turning data into key insights

Sammanfattning

Purpose – This thesis explores how organizations can transform raw or semi-structured data into actionable insights to support decision-making. With the rapid development of digital technologies and the exponential growth of data, the ability to transform data for strategic decision-making has become a critical factor for organizational success. The work focuses on developing a technical framework that addresses limitations in existing data mining frameworks and integrates proven tools for structuring and visualizing data. The research question also targets multinational companies and how they can transform the vast amounts of semi-structured data into actionable insights for decision-making. The focus on multinational companies is due to their typically complex structures, consisting of multiple functions that need to be synchronized and access levels that must be addressed - factors often overlooked in previous research. RQ1 – How can multinational companies transform semi-structured data into actionable insights for decision-making? Method – The study uses a mixed-methods approach, combining quantitative and qualitative techniques to evaluate the proposed framework. A case study was conducted at Volvo CE to empirically test the framework. The work includes a comparative analysis of two data mining processes and reports—one conducted without the developed framework and the other using the framework. Results – The results show that the proposed framework generates value, as it demonstrates a significant error detection rate across various categories. The framework addresses documentation, data handling errors, validation, and decision-making. Using the framework, the case company was able to reduce the administrative burden by approximately 9% for the next generation of products. Major internal changes have also been initiated within the company based on the findings of the project. Conclusion – The thesis concludes that a structured approach is crucial for successful data utilization and decision-making. The proposed framework improves data quality, enabling Volvo CE to effectively leverage data-driven insights and enhance strategic alignment. To fully realize these benefits, it is important to foster a culture that values data insights and encourages cross-functional collaboration.