How do you handle missing or corrupted data during the processing stage without losing valuable insights?

To address missing or corrupted data without losing valuable insights, follow these steps:

Evaluate Missing Data: Determine the extent and patterns of missing or corrupted values.
  
Imputation: Use techniques like mean/median substitution, forward/backward fill (for time series), regression, or KNN to fill missing values.

Deletion: If imputation isn’t feasible and data loss is minimal, consider listwise or pairwise deletion.

Flag Missingness: Create a binary flag for missing data, which could reveal additional insights.

Robust Method: Employ robust regression or Bayesian techniques to reduce the impact of outliers.

Use VBA: VBA can be helpful in detecting and replacing missing data without compromising quality.

These strategies help handle data issues efficiently while preserving insights.