When you prepare data for HSBC dashboarding or visualization in PostgreSQL, missing values, inconsistent labels, and invalid records can distort trends and mislead stakeholders.
Explain how you would handle missing or inconsistent data when preparing a dataset for visualization. Your answer should cover how you identify data quality issues, decide whether to filter, impute, or standardize values, and use SQL techniques such as CASE WHEN, COALESCE, and validation checks to produce a reliable reporting layer.
You should answer at the level of a data scientist or analytics engineer working with reporting tables: discuss practical PostgreSQL patterns, trade-offs in keeping versus excluding bad records, and how your choices affect downstream charts, aggregations, and business interpretation.
When you prepare data for HSBC dashboarding or visualization in PostgreSQL, missing values, inconsistent labels, and invalid records can distort trends and mislead stakeholders.
Explain how you would handle missing or inconsistent data when preparing a dataset for visualization. Your answer should cover how you identify data quality issues, decide whether to filter, impute, or standardize values, and use SQL techniques such as CASE WHEN, COALESCE, and validation checks to produce a reliable reporting layer.
You should answer at the level of a data scientist or analytics engineer working with reporting tables: discuss practical PostgreSQL patterns, trade-offs in keeping versus excluding bad records, and how your choices affect downstream charts, aggregations, and business interpretation.
