Intelligent Process Automation for Data Lifecycle Management (Data Retention and Data Destruction) Through Process Mining

In an era of rapidly growing data, effective data lifecycle management has become crucial for organizations. This paper addresses the challenge of identifying and classifying data columns as either demographic or transactional across various systems, where column names may differ significantly (e.g., "Sex" in one system and "Gender" in another). The purpose of this research is to develop a model that can accurately classify these data columns, enabling automated data retention and destruction processes. The proposed model leverages intelligent process automation and process mining to identify and categorize data, allowing transactional data to be archived automatically after a specified timeframe. By implementing this model, organizations can improve their data management efficiency, ensuring compliance with data retention policies while optimizing storage use.