Data Exploration and Quality Assessment Questions

Investigate a dataset thoroughly before analysis or reporting by profiling its structure, contents, and reliability. Typical steps include examining row counts and data volume, inspecting column data types and sample values, validating date formats and ranges, and identifying missing values, duplicates, outliers, and impossible values. Understand schema and relationships between tables or files, check data freshness and latency, and characterize data completeness and coverage with simple metrics and queries. Document discovered issues, their likely causes and impacts on conclusions, and pragmatic workarounds or transformation strategies to mitigate risk. Use exploratory queries and summary statistics to quantify data quality, note limitations and assumptions, and allocate an appropriate portion of case study time to data assessment before proceeding to modeling or visualization.