Data Archival Methodology in Enterprise Resource Planning Applications (Oracle ERP, Peoplesoft)

Enterprise Resource Planning (ERP) applications such as Oracle ERP and PeopleSoft play a critical role in managing and organizing vast amounts of data for businesses. As the volume of data within these applications continues to grow, organizations face challenges related to data storage, performance, and compliance with data retention policies. A robust data archival methodology is essential to address these challenges and ensure efficient and effective data management. This paper presents a comprehensive study on data archival methodology specifically tailored for Oracle ERP and PeopleSoft applications. The methodology encompasses a systematic approach to identify, categorize, and archive data based on its relevance, usage, and regulatory requirements. It focuses on achieving optimal data retention and retrieval while minimizing the impact on system performance and storage requirements. The proposed methodology covers various aspects of data archival, including data identification and classification, archival policies and rules, archival strategies, and post-archival data management. It explores techniques for selecting and extracting data from the ERP applications, transforming it into a suitable archival format, and securely storing it in archival repositories or storage systems. The paper also discusses considerations for indexing, searching, and retrieving archived data efficiently. Furthermore, the study highlights the importance of data integrity, security, and compliance in the archival process. It addresses strategies for ensuring the integrity and authenticity of archived data, implementing access controls and encryption mechanisms, and adhering to regulatory requirements, such as data privacy and protection regulations. To validate the effectiveness of the proposed methodology, case studies and real-world examples are presented, demonstrating its successful implementation in Oracle ERP and PeopleSoft environments. The benefits of the methodology, including improved system performance, reduced storage costs, and simplified compliance with data retention policies, are discussed based on these case studies.