| name | data-exploration |
| description | Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze. |
| author | Perplexity |
| version | 1.0 |
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]
