Data Profiling

Structural Reconnaissance

Table-Level Inventory

Start every new dataset encounter by answering these questions:

• What is the total row and column count?
• What does each row represent (the grain)?
• Which column or columns uniquely identify a row?
• When was the most recent data loaded?
• What is the earliest date in the dataset?

Classifying Columns by Role

Assign every column to one of these categories:

• Key: Primary keys, foreign keys, entity identifiers
• Attribute: Categorical fields used for grouping or filtering (region, status, plan_type)
• Measure: Numeric fields intended for aggregation (revenue, duration, score)
• Timestamp: Date or datetime fields (created_at, processed_on, event_date)
• Free-text: Unstructured strings (descriptions, comments, names)
• Flag: Binary true/false indicators
• Nested: JSON blobs, arrays, or embedded structures

Column-Level Inspection

Universal Checks (All Column Types)

• Null count and null percentage
• Count of distinct values and cardinality ratio (distinct / total rows)
• Top 5-10 most frequent values with their occurrence counts
• Bottom 5 least frequent values (useful for spotting anomalies)

Numeric Measures

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Minimum, maximum, mean, median
Standard deviation
Percentile ladder: p1, p5, p25, p75, p95, p99
Count of zeros
Count of negatives (flag if unexpected)

Text and Categorical Fields

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Shortest length, longest length, average length
Count of empty strings
Format regularity (do values follow a consistent pattern?)
Case consistency (uniform upper, uniform lower, or mixed?)
Count of values with leading or trailing whitespace

Date and Timestamp Fields

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Earliest date, latest date
Count of nulls
Count of future dates (flag if the domain forbids them)
Distribution across months or weeks
Gaps in expected daily/weekly cadence

Boolean Fields

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True count, false count, null count
True proportion

Discovering Relationships

After examining columns individually, look for connections:

• Foreign key candidates: ID columns whose values likely reference another table
• Hierarchical dimensions: Columns that nest naturally (country > region > city)
• Correlated measures: Numeric columns that trend together
• Computed columns: Fields that appear derived from other columns in the same table
• Duplicated information: Columns that carry identical or near-identical content

Evaluating Data Quality

Completeness Ratings

Assign each column a tier:

• Full (>99% populated): No action needed
• Mostly full (95-99%): Investigate the missing values
• Gaps present (80-95%): Determine whether the gaps are systematic and whether the column is still usable
• Sparse (<80%): Likely unusable without imputation or supplemental data

Consistency Checks

Scan for:

• Value normalization failures: The same concept spelled multiple ways ("USA", "US", "United States", "us")
• Type mismatches: Numerics stored as text, dates in inconsistent formats
• Broken references: Foreign key values with no matching parent record
• Rule violations: Negative quantities, end dates preceding start dates, percentages outside 0-100
• Cross-column contradictions: status = 'shipped' while ship_date is null

Accuracy Warning Signs

Patterns that suggest the data may be unreliable:

• Sentinel values: 0, -1, 999999, "N/A", "TBD", "test", "xxx"
• Suspiciously dominant defaults: One value appearing far more often than expected
• Stale records: The updated_at column shows no recent activity in an active system
• Physically impossible values: Ages above 150, dates decades in the future, negative durations
• Rounding artifacts: Disproportionate clustering on multiples of 5 or 10 (implies estimation)

Freshness Assessment

• What is the timestamp of the most recent row?
• How often should this table refresh?
• Is there measurable lag between event occurrence and warehouse arrival?
• Are there missing days or hours in the time series?

Recognizing Patterns

Distribution Shapes

When profiling a numeric column, classify its shape:

• Bell-shaped: Mean and median nearly equal; symmetric tails
• Right-skewed: Long right tail with a few very large values (typical for revenue, session length)
• Left-skewed: Long left tail with a few very small values (less common)
• Bimodal: Two distinct peaks (suggests two merged populations)
• Power-law: A handful of enormous values dominating; many near-zero values (user engagement metrics)
• Uniform: Roughly flat across the range (often synthetic or randomly generated)

Time-Based Patterns

For any temporal data, investigate:

• Trend: Persistent upward or downward drift
• Seasonality: Recurring cycles — weekly, monthly, quarterly, annual
• Weekday effects: Systematic weekday vs. weekend differences
• Holiday impacts: Spikes or dips around known holidays
• Level shifts: Abrupt, sustained changes in the baseline
• Isolated anomalies: Single data points that break the prevailing pattern

Segment Discovery

Surface natural groupings by:

• Identifying categorical columns with 3 to 20 distinct values
• Comparing metric distributions across each segment value
• Highlighting segments whose behavior diverges meaningfully from the overall
• Checking whether apparent segments contain meaningful sub-segments

Correlation Scanning

Across numeric columns:

• Build a pairwise correlation matrix
• Highlight strong associations (|r| > 0.7)
• Remember that correlation never establishes causation — note this explicitly
• Probe for non-linear relationships (quadratic, logarithmic) that Pearson r would miss

Documenting What You Find

Dataset Summary Template

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## Table: [schema.table_name]

**Purpose**: [What this table captures]
**Grain**: [One row per...]
**Primary Key**: [column(s)]
**Approximate Rows**: [count, as of date]
**Refresh Cadence**: [real-time / hourly / daily / weekly]
**Responsible Team**: [owner]

### Important Columns

| Column | Type | Meaning | Sample Values | Notes |
|--------|------|---------|---------------|-------|
| user_id | STRING | Unique user handle | "usr_abc123" | References users.id |
| event_type | STRING | Action category | "click", "view", "purchase" | 15 distinct values |
| revenue | DECIMAL | USD transaction amount | 29.99, 149.00 | Null for non-purchases |
| created_at | TIMESTAMP | Event occurrence time | 2024-01-15 14:23:01 | Partition column |

### Join Paths
- Links to `users` via `user_id`
- Links to `products` via `product_id`
- Parent of `event_details` (one-to-many on event_id)

### Known Caveats
- [Document any quality issues]
- [Note analytical gotchas]

### Typical Query Use Cases
- [List common analytical patterns against this table]

Schema Discovery Queries

When working directly against a warehouse, use these patterns:

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

-- Inspect column metadata (PostgreSQL)
SELECT column_name, data_type, is_nullable, column_default
FROM information_schema.columns
WHERE table_name = 'target_table'
ORDER BY ordinal_position;

-- Rank tables by storage footprint (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 count per table (general approach)
-- Execute individually: SELECT COUNT(*) FROM table_name

Tracing Data Lineage

When navigating an unfamiliar warehouse:

1. Begin at the consumption layer — identify which tables power reports and dashboards
2. Follow dependencies upstream: what feeds those tables?
3. Map the raw / staging / mart architecture
4. Track where transformations enrich, filter, or roll up the data
5. Record any points where data is joined with external sources