Understanding Data

About

This skill provides a structured framework for exploring unfamiliar datasets through a comprehensive four-phase profiling process. It guides users through schema discovery, data quality assessment, distribution analysis, and relationship identification using database-agnostic SQL patterns. By establishing a clear checklist and documenting findings in a standardized summary, it ensures that data analysis begins with a solid understanding of table cardinalities, missing values, distribution shapes, and referential integrity, ultimately preventing errors caused by hidden data anomalies or incorrect structural assumptions.

Key Features

• Systematic 4-phase data profiling methodology
• Automated data quality checks for NULLs, empty strings, and outliers
• Foreign key discovery and join cardinality verification
• Multi-dialect SQL support for major relational databases including SQLite, PostgreSQL, and MySQL
• Distribution analysis for temporal patterns and categorical segmentation
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Use Cases

• Starting a new analysis session with an unfamiliar database or dataset
• Validating data integrity and relationships before building complex reporting queries
• Investigating unexpected query results or suspicious data patterns