In analytics work, incomplete or inconsistent data can distort metrics, break queries, and lead to bad decisions. You need a practical way to assess the issue before trusting the dataset.
How would you respond if the data you were given looked incomplete or inconsistent? Explain how you would identify the problem, decide whether the issue is missing values, duplicate rows, invalid formats, or conflicting records, and describe how you would communicate the risk before using the data.
Keep your answer focused on SQL/data validation and cleanup thinking. The interviewer is looking for a clear, structured approach to checking data quality, not a deep statistics discussion or a full ETL design.
