Missing Data

Missing data is one of the most common and critical problems in data cleaning. Empty fields are not just technical gaps; they may reflect operational issues in data generation, form design errors, integration failures, or user behavior patterns. For that reason, the first step is to understand why the data is missing, and only then consider strategies such as deletion, imputation, or treating missingness as its own category. Whether the missingness is random or systematic directly affects the appropriate handling approach.