Missing values are common in reporting datasets and can distort downstream dashboards if they are ignored or handled inconsistently. In a retail analytics setting, this often affects product, inventory, and transaction reporting.
You are given tables feeding a merchandising dashboard, including NIKE product, order, and inventory data, and some fields contain missing values. Explain how you would approach this in SQL. You should cover how you would identify where missingness exists, distinguish between acceptable nulls and data quality issues, choose between filtering, imputing, defaulting, or preserving nulls, and document those choices for downstream consumers.
The interviewer expects a practical SQL-oriented explanation rather than a generic data science answer. Focus on profiling patterns of missing data, using SQL constructs to standardize handling logic, and explaining trade-offs in reporting accuracy versus completeness.