To address these challenges, this study develops a practical solution in the form of an Excel workbook. Designed as a comprehensive guideline, the workbook offers a structured approach to enhancing the transparency of predictive analytics. In collaboration with two clients, specific manifestations of these challenges were identified and addressed within the workbook, ensuring that it is grounded in real-world business needs. The workbook provides clear documentation, practical examples, and visual aids to help businesses better understand and trust the models they deploy.

Although the workbook has not yet been tested in the field, it is expected to significantly improve stakeholder confidence in predictive analytics by making these processes more transparent and accessible. Further research and testing will be necessary to assess its effectiveness in real-world business environments. This thesis contributes to the ongoing effort to bridge the gap between complex analytical models and practical business applications, aiming to foster more informed and confident decision-making in the future.