The abstract theory governing missing value management in data pipelines dictates that null values and empty strings represent distinct states—unknown versus defined-but-empty—which must be standardized prior to aggregation or visualization. The core mechanism involves a sequential cleaning protocol where whitespace is eliminated via trimming before character replacement, ensuring consistent type interpretation for text, numeric, and temporal domains. This theoretical framework establishes that the strategic substitution of missing indicators with either nulls or dummy default values (e.g., 'NA', zero, epoch dates) directly determines the mathematical validity of subsequent analytical operations within business intelligence disciplines.
The abstract theory governing missing value management in data pipelines dictates that null values and empty strings represent distinct states—unknown versus defined-but-empty—which must be standardi…