data-exploration

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.

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: Categorize each column as one of:

• Identifier: Unique keys, foreign keys, entity IDs
• Dimension: Categorical attributes for grouping/filtering (status, type, region, category)
• Metric: Quantitative values for measurement (revenue, count, duration, score)
• Temporal: Dates and timestamps (created_at, updated_at, event_date)
• Text: Free-form text fields (description, notes, name)
• 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 (distinct / total)
• Most common values (top 5-10 with frequencies)
• Least common values (bottom 5 to spot anomalies)

Numeric columns (metrics):

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

String columns (dimensions, text):

min length, max length, avg length
empty string count
pattern analysis (do values follow a format?)
case consistency (all upper, all lower, mixed?)
leading/trailing whitespace count

Date/timestamp columns:

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

Boolean columns:

true count, false count, null count
true rate

Phase 3: Relationship Discovery

After profiling individual columns:

• Foreign key candidates: ID columns that might link to other tables
• Hierarchies: Columns that form natural drill-down paths (country > state > city)
• 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

Rate each column:

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

Consistency Checks

Look for:

• Value format inconsistency: Same concept represented differently ("USA", "US", "United States", "us")
• Type inconsistency: Numbers stored as strings, dates in various formats
• Referential integrity: Foreign keys that don't match any parent record
• Business rule violations: Negative quantities, end dates before start dates, percentages > 100
• Cross-column consistency: Status = "completed" but completed_at is null

Accuracy Indicators

Red flags that suggest accuracy issues:

• Placeholder values: 0, -1, 999999, "N/A", "TBD", "test", "xxx"
• Default values: Suspiciously high frequency of a single value
• Stale data: Updated_at shows no recent changes in an active system
• Impossible values: Ages > 150, dates in the far future, negative durations
• Round number bias: All values ending in 0 or 5 (suggests estimation, not measurement)

Timeliness Assessment

• When was the table last updated?
• What is the expected update frequency?
• Is there a lag between event time and load time?
• Are there gaps in the time series?

Pattern Discovery Techniques

Distribution Analysis

For numeric columns, characterize the distribution:

• Normal: Mean and median are close, bell-shaped
• Skewed right: Long tail of high values (common for revenue, session duration)
• Skewed left: Long tail of low values (less common)
• Bimodal: Two peaks (suggests two distinct populations)
• Power law: Few very large values, many small ones (common for user activity)
• Uniform: Roughly equal frequency across range (often synthetic or random)

Temporal Patterns

For time series data, look for:

• Trend: Sustained upward or downward movement
• Seasonality: Repeating patterns (weekly, monthly, quarterly, annual)
• Day-of-week effects: Weekday vs. weekend differences
• Holiday effects: Drops or spikes around known holidays
• Change points: Sudden shifts in level or trend
• Anomalies: Individual data points that break the pattern

Segmentation Discovery

Identify natural segments by:

• Finding categorical columns with 3-20 distinct values
• Comparing metric distributions across segment values
• Looking for segments with significantly different behavior
• Testing whether segments are homogeneous or contain sub-segments

Correlation Exploration

Between numeric columns:

• Compute correlation matrix for all metric pairs
• Flag strong correlations (|r| > 0.7) for investigation
• Note: Correlation does not imply causation -- flag this explicitly
• Check for non-linear relationships (e.g., quadratic, logarithmic)

Schema Understanding and Documentation

Schema Documentation Template

When documenting a dataset for team use:

## 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]
**Owner**: [team or person responsible]

### Key Columns

| Column | Type | Description | Example Values | Notes |
|--------|------|-------------|----------------|-------|
| user_id | STRING | Unique user identifier | "usr_abc123" | FK to users.id |
| event_type | STRING | Type of event | "click", "view", "purchase" | 15 distinct values |
| revenue | DECIMAL | Transaction revenue in USD | 29.99, 149.00 | Null for non-purchase events |
| created_at | TIMESTAMP | When the event occurred | 2024-01-15 14:23:01 | Partitioned on this column |

### Relationships
- Joins to `users` on `user_id`
- Joins to `products` on `product_id`
- Parent of `event_details` (1:many on event_id)

### Known Issues
- [List any known data quality issues]
- [Note any gotchas for analysts]

### Common Query Patterns
- [Typical use cases for this table]

Schema Exploration Queries

When connected to a data warehouse, use these patterns to discover schema:

-- List all tables in a schema (PostgreSQL)
SELECT table_name, table_type
FROM information_schema.tables
WHERE table_schema = 'public'
ORDER BY table_name;

-- Column details (PostgreSQL)
SELECT column_name, data_type, is_nullable, column_default
FROM information_schema.columns
WHERE table_name = 'my_table'
ORDER BY ordinal_position;

-- Table sizes (PostgreSQL)
SELECT relname, pg_size_pretty(pg_total_relation_size(relid))
FROM pg_catalog.pg_statio_user_tables
ORDER BY pg_total_relation_size(relid) DESC;

-- Row counts for all tables (general pattern)
-- Run per-table: SELECT COUNT(*) FROM table_name

Lineage and Dependencies

When exploring an unfamiliar data environment:

1. Start with the "output" tables (what reports or dashboards consume)
2. Trace upstream: What tables feed into them?
3. Identify raw/staging/mart layers
4. Map the transformation chain from raw data to analytical tables
5. Note where data is enriched, filtered, or aggregated