Data Validation
The process of checking data against rules and constraints to ensure accuracy, completeness, and consistency.
In-Depth Explanation
Data validation verifies that data meets defined criteria before it's used. It catches problems early, preventing invalid data from corrupting downstream systems and analysis.
Validation types:
- Type checking: Correct data type (string, number, date)
- Range checking: Values within acceptable bounds
- Format validation: Matches expected pattern (email, phone)
- Referential integrity: Foreign keys exist
- Business rules: Domain-specific constraints
- Consistency checks: Related fields are coherent
Validation timing:
- At entry: Forms, APIs validate input
- At ingestion: ETL checks incoming data
- At rest: Periodic quality scans
- Before use: Pre-processing validation
Implementation approaches:
- Database constraints
- Application validation
- Data quality platforms
- Schema validation (JSON Schema, Pydantic)
- Great Expectations, dbt tests
Business Context
Validation prevents bad data from entering systems, catching problems when they're cheap to fix rather than after they've caused damage.
How Clever Ops Uses This
We implement data validation at multiple points in Australian business data pipelines, ensuring AI systems receive quality inputs.
Example Use Case
"Validating API inputs: ensuring email format is correct, required fields present, values within allowed ranges, before processing."
Frequently Asked Questions
Related Terms
Related Resources
Data Quality
The measure of data fitness for its intended purpose. High-quality data is accur...
Data Cleaning
The process of detecting and correcting errors, inconsistencies, and inaccuracie...
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