Data Validation
14 articles
- May 21, 2026
Enum Validation: How to Ensure Fields Only Contain Allowed Values
A status field with 47 distinct values when you only have 6 statuses. Enum validation eliminates categorical data chaos automatically.
- May 21, 2026
How to Use Regex for Data Validation Without Being a Developer
Regular expressions are the most powerful tool for pattern-based data validation — and you don't need to be a developer to use the most common ones. Here's a practical guide.
- May 21, 2026
How to Write Effective Data Validation Rules for Your Business
Most validation rules either catch nothing useful or flag so many false positives they get ignored. Here's how to write rules that actually catch the problems that matter.
- May 21, 2026
How to Set Acceptable Error Rates for Your Data Quality Rules
Zero errors isn't a realistic threshold — and chasing it produces a monitoring system nobody trusts. Here's how to set error rate thresholds that are meaningful and actually used.
- May 21, 2026
How to Build a Custom Data Quality Rule Library
A rule library is the difference between data quality checks that run once and checks that scale across every dataset your team touches. Here's how to build one that gets used.
- May 21, 2026
Threshold-Based Data Quality: When Is 'Good Enough' Actually Good Enough?
Perfect data quality is neither achievable nor necessary. The question is where to draw the line — and it should be based on business impact, not universal standards.
- May 21, 2026
Range Validation: Ensuring Numeric Data Stays Within Bounds
A price of -$500. A percentage of 150%. An employee age of 847. Range validation stops these from entering your systems automatically.
- May 21, 2026
How to Validate Date Formats Automatically
Mixed date formats are one of the most common data quality problems — and one of the easiest to prevent automatically. Here's how to catch them before they break your imports.
- May 21, 2026
Referential Integrity: What It Is and Why It Breaks
Referential integrity failures are invisible in your data but visible in your reports — NULL values where customer names should be, broken joins that drop revenue from your analytics.
- May 21, 2026
Pattern Matching for Data Quality: A Practical Guide
Pattern matching validates that field values follow a specific structural pattern — catching postal codes with letters, order IDs in the wrong format, and phone numbers that are clearly wrong.
- May 21, 2026
How to Validate Third-Party Data Before You Trust It
Third-party data arrives with implicit trust you haven't earned. Before loading a vendor file, enrichment dataset, or purchased list, these are the checks that protect you.
- May 21, 2026
What Is Data Validation? A Complete Guide
Data validation checks whether values in a dataset meet defined rules before they're used — catching errors before they cause damage. Here's everything you need to know.
- May 21, 2026
How to Validate Email Addresses at Scale
Validating 50,000 emails — while preserving deliverability and not sending data to a third-party server — is where most teams struggle. Here's how to do it right.
- May 21, 2026
Cross-Field Validation: When One Data Field Depends on Another
Cross-field validation catches errors that single-field rules miss — like a 'Cancelled' subscription with no cancellation date. Here's how to design these rules.