Comparative Analysis of AI-Driven Imputation Methods: Enhancing Data Quality in Big Data Environments

Abstract

In today's data-driven world, the quality of data plays a critical role in the effectiveness of analytical processes and decision-making. Missing data is a prevalent issue that can severely impact the integrity of data analyses. This paper presents a comprehensive comparative analysis of various AI-driven imputation methods for handling missing data in big data environments. We evaluate traditional imputation techniques alongside modern AI approaches, including machine learning algorithms and deep learning models, to assess their effectiveness in enhancing data quality. By analyzing performance metrics such as accuracy, computational efficiency, and scalability, we provide insights into the strengths and weaknesses of these methods, thereby guiding practitioners in selecting the most appropriate imputation strategy for their specific data contexts.