Evaluating Data Imputation Methods for Missingness
In analytics, few challenges are as persistent and unavoidable as missing data. Whether it arises from incomplete surveys, sensor malfunctions, or human error, missingness has the potential to distort analyses and compromise the reliability of insights. For a data analyst, choosing the right imputation method — the process of filling in missing values — is critical to ensuring accuracy and integrity in decision-making. This article explores the theoretical foundation of imputation techniques, their strengths and limitations, and how professionals can prepare to handle these challenges effectively.