Advanced Missing Data

Techniques for handling missing information include imputation (mean, median, or model-based), deletion of incomplete records, or flagging missing fields.

Each method impacts profiling and validation differently: imputation can preserve dataset size but may introduce bias (depending on how much data is missing), while deletion may improve quality at the cost of reducing sample size.

Like with everything in this field, there is no single best solution to all problems, instead, consider that the best approach depends on your context.