Revolutionizing Data Management: An Analysis of Automated Front-End Processes in the Data Life Cycle Using Machine Learning

Companies must focus on managing the data life cycle efficiently and reliably to obtain valuable insights, especially with the increasing amount of data generated. This work emphasizes the importance of automating the initial stages of the data life cycle, such as data collection, ingestion, cleaning, preprocessing, and integration. Organizations must focus on managing data throughout their life cycles to gain valuable insights, especially with the increasing amount of data available. This work features the significance of computerizing the front-end periods of the information life cycle, which incorporate information assortment, ingestion, cleaning, preprocessing, and reconciliation. Thus, associations can guarantee viable and solid administration of their information. The motivation behind this study is to evaluate the achievability and knowledge of the future headings of information life cycle mechanization.