Data Exploration Skill

Systematic methodology for profiling datasets, assessing data quality, discovering patterns, and understanding schemas.

Data Profiling Methodology

Phase 1: Structural Understanding

Before analyzing any data, understand its structure:

Table-level questions:

• How many rows and columns?
• What is the grain (one row per what)?
• What is the primary key? Is it unique?
• When was the data last updated?
• How far back does the data go?

Column classification:

• Identifier: Unique keys, foreign keys, entity IDs
• Dimension: Categorical attributes for grouping/filtering
• Metric: Quantitative values for measurement
• Temporal: Dates and timestamps
• Text: Free-form text fields
• Boolean: True/false flags
• Structural: JSON, arrays, nested structures

Phase 2: Column-Level Profiling

For each column, compute:

All columns:

• Null count and null rate
• Distinct count and cardinality ratio
• Most common values (top 5-10 with frequencies)
• Least common values (bottom 5 to spot anomalies)

Numeric columns:

• min, max, mean, median (p50)
• standard deviation
• percentiles: p1, p5, p25, p75, p95, p99
• zero count, negative count

String columns:

• min length, max length, avg length
• empty string count
• pattern analysis, case consistency

Date/timestamp columns:

• min date, max date, null dates
• future dates (if unexpected)
• distribution by month/week, gaps in time series

Phase 3: Relationship Discovery

• Foreign key candidates: ID columns that might link to other tables
• Hierarchies: Columns that form natural drill-down paths
• Correlations: Numeric columns that move together
• Derived columns: Columns that appear to be computed from others
• Redundant columns: Columns with identical or near-identical information

Quality Assessment Framework

Completeness Score

• Complete (>99% non-null): Green
• Mostly complete (95-99%): Yellow - investigate the nulls
• Incomplete (80-95%): Orange - understand why
• Sparse (<80%): Red - may not be usable without imputation

Consistency Checks

• Value format inconsistency ("USA", "US", "United States")
• Type inconsistency: Numbers stored as strings, dates in various formats
• Business rule violations: Negative quantities, end dates before start dates
• Cross-column consistency: Status = "completed" but completed_at is null

Accuracy Indicators

Red flags:

• Placeholder values: 0, -1, 999999, "N/A", "TBD", "test"
• Default values: Suspiciously high frequency of a single value
• Stale data: Updated_at shows no recent changes
• Impossible values: Ages > 150, dates in the far future

Pattern Discovery

Distribution Analysis

• Normal: Mean and median are close, bell-shaped
• Skewed right: Long tail of high values (common for revenue)
• Power law: Few very large values, many small ones
• Bimodal: Two peaks (suggests two distinct populations)

Temporal Patterns

• Trend, seasonality, day-of-week effects, holiday effects
• Change points: Sudden shifts in level or trend
• Anomalies: Individual data points that break the pattern

Schema Documentation Template

## Table: [schema.table_name]

**Description**: [What this table represents]
**Grain**: [One row per...]
**Primary Key**: [column(s)]
**Row Count**: [approximate, with date]
**Update Frequency**: [real-time / hourly / daily / weekly]

### Key Columns

| Column | Type | Description | Example Values | Notes |
|--------|------|-------------|----------------|-------|
| user_id | STRING | Unique user identifier | "usr_abc123" | FK to users.id |

### Known Issues
- [List any known data quality issues]