Data preparation often matters more than the final analysis. In tools like McKinsey & QuantumBlack workflows, poor handling of missing values or highly skewed distributions can distort summary metrics, segment comparisons, and downstream models.
Explain how you would handle missing data and highly skewed data when preparing a dataset for analysis in SQL. Your answer should cover how you would identify the issue, decide whether to filter, impute, cap, transform, or flag values, and how you would preserve analytical transparency.
You should answer at a practical analyst level: describe the decision process, trade-offs, and the SQL techniques you would use. It is helpful to mention how CASE WHEN, COALESCE, and aggregate checks can support data quality review before analysis.
Data preparation often matters more than the final analysis. In tools like McKinsey & QuantumBlack workflows, poor handling of missing values or highly skewed distributions can distort summary metrics, segment comparisons, and downstream models.
Explain how you would handle missing data and highly skewed data when preparing a dataset for analysis in SQL. Your answer should cover how you would identify the issue, decide whether to filter, impute, cap, transform, or flag values, and how you would preserve analytical transparency.
You should answer at a practical analyst level: describe the decision process, trade-offs, and the SQL techniques you would use. It is helpful to mention how CASE WHEN, COALESCE, and aggregate checks can support data quality review before analysis.
