In real companies, data doesn’t go straight from source to dashboard.

It goes through validation.

Because bad data in production doesn’t just cause wrong charts — it causes:

• Bad decisions
• Broken pipelines
• Lost trust

Here are the data validation checks companies actually use in production systems.

What Is Data Validation in Production?

Data validation ensures that incoming data:

• Matches expectations
• Follows rules
• Is safe to use downstream

These checks run automatically, often before data is stored or reported.

1. Schema Validation

Checks whether:

• Expected columns exist
• Data types match (int, string, date, etc.)

Prevents pipelines from breaking due to schema drift.

2. Null Value Checks

Ensures required fields are not missing.

Example:

• user_id
• order_date
• transaction_amount

Nulls in critical columns can invalidate entire analyses.

3. Uniqueness Checks

Confirms that values meant to be unique actually are.

Common examples:

• Primary keys
• Order IDs
• User IDs

Duplicates often signal upstream issues.

4. Range Validation

Validates numeric values fall within acceptable limits.

Examples:

• Age between 0 and 120
• Prices ≥ 0
• Percentages between 0 and 100

Out-of-range values are red flags.

5. Data Type Enforcement

Ensures fields are stored in the correct format.

Examples:

• Dates as dates, not strings
• Numbers not mixed with text

This prevents silent calculation errors.

6. Referential Integrity Checks

Verifies relationships between tables.

Example:

• Every customer_id in orders exists in customers

Broken references cause inaccurate joins.

7. Accepted Value Checks

Restricts columns to known values.

Examples:

• Status = pending, completed, cancelled
• Country codes from approved list

Unexpected values often indicate source errors.

8. Freshness Checks

Ensures data arrives on time.

Example:

• Daily data loaded within 24 hours

Late data can silently break dashboards.

9. Volume Checks

Validates record counts are within expected ranges.

Example:

• Today’s data volume should not drop 90% overnight

Sudden spikes or drops usually mean pipeline issues.

10. Duplicate Record Checks

Detects repeated rows beyond expected thresholds.

Duplicates inflate metrics like:

• Revenue
• User counts
• Event totals

11. Pattern Matching Checks

Uses regex or rules to validate formats.

Examples:

• Email addresses
• Phone numbers
• IDs

Catches malformed data early.

12. Consistency Checks Across Fields

Validates logical relationships.

Examples:

• start_date ≤ end_date
• discounted_price ≤ original_price

Logical errors often slip past type checks.

13. Historical Trend Checks

Compares new data to historical behavior.

Example:

• Sales usually change gradually, not 10x overnight

Anomaly detection protects against silent failures.

14. Business Rule Validation

Encodes domain-specific logic.

Examples:

• Refunds cannot exceed payments
• Inactive users should not generate events

These checks reflect how the business actually works.

What Happens Without Validation

Without these checks:

• Dashboards lie
• Models degrade
• Stakeholders stop trusting data

And the damage is often noticed too late.

What This Means for Analysts

Even if you don’t build pipelines:

• You should understand validation
• You should question unchecked data
• You should spot validation gaps

This is what separates toy analysis from production analytics.

Production data is messy by default.

Validation isn’t optional, it’s protection.

The best teams don’t just analyze data.
They defend its quality.

FAQs