Refining the Knowledge Discovery Process: Introducing a Data Management Phase for Ethical and Efficient Post- Analysis Data Handling

Abstract

In the evolving landscape of Knowledge Discovery in Databases (KDD), the efficient management of data post-discovery emerges as a pivotal yet often overlooked consideration. This research proposes an innovative extension to the conventional KDD process by introducing a "Data Management" focused on the crucial decision-making regarding data deletion or data compression after knowledge has been extracted. This addition aims to address the multifaceted challenges of ethical considerations, security vulnerabilities, and storage inefficiencies that accompany large datasets in the post-analysis phase. Through a comprehensive exploration of the KDD process, including an analysis of existing methodologies and the integration of our proposed step, we elucidate the significant impact of data management on enhancing the ethical, secure, and efficient utilization of database information. Our findings highlight the necessity for a standardized approach to data retention or reduction, providing clear guidelines that balance organizational needs with legal and ethical standards. This paper not only contributes to the academic discourse on KDD process optimization but also offers practical insights for organizations striving to navigate the complexities of data management in an ethically responsible and resource-efficient manner.