Development of a Technical Solution Concept for Process and Data Mining in Organizations Focused and Applied on E-Mail Processes

This paper embarks on a transformative exploration of decision-making paradigms in contemporary organizations. It presents an incisive study into the compelling dichotomy between traditional and Artificial Intelligence (AI)-enhanced methodologies. The initial part of the investigation brings to light the instrumental role of process and data mining in effective business operations management. By illuminating their significance, the paper sets the stage for a deeper investigation into how these tools can be used to drastically improve organizational efficiency and decision-making processes. The significance of process mining and data mining in organizations is elucidated initially, underlining their instrumental role in the efficacious management of business operations [1]. The paper then proceeds to implement process mining methodologies on an anonymized dataset of e-mails, crafting a dynamic business process characterized by continual metrics analysis and supervision. This approach underscores the potency of process mining in delivering actionable insights from complex datasets, significantly improving operational efficiency [2]. Subsequently, the paper presents the application of data mining via Support Vector Machine (SVM) on the e-mail data. SVM, a prominent machine learning algorithm, is known for its robustness in high-dimensional spaces and effectiveness in classification tasks [3]. Leveraging this, the study demonstrates the profound potential for automation, offering a tantalizing glimpse into future organizational operations. Through this dual-pronged examination of process mining and SVM-based data mining, the paper illuminates the transformative potential of AI in redefining decision-making processes in management. It underscores the capabilities of AI to transcend traditional boundaries and catalyze a new era of data-driven, automated decision-making.