Data Quality
Data Quality
Section titled “Data Quality”Data validation, cleansing rules, quality scoring, and data governance for the IOF platform.
Overview
Section titled “Overview”The Data Quality rail ensures data integrity across the platform:
- Validation rules — Define and enforce data validation rules per entity type
- Quality scoring — Calculate data completeness, accuracy, and consistency scores
- Cleansing workflows — Automated data cleansing pipelines for incoming records
- Anomaly detection — Identify outliers and suspicious data patterns
- Data lineage — Track data provenance and transformation history
Key Concepts
Section titled “Key Concepts”| Concept | Description |
|---|---|
| Completeness | Percentage of required fields populated |
| Accuracy | Correctness of data values against reference sources |
| Consistency | Data agreement across different systems and tables |
| Timeliness | How current the data is relative to expectations |
| DQ Score | Composite data quality score (0-100) |
Endpoints
Section titled “Endpoints”Refer to the API Explorer for interactive endpoint documentation.
Authentication
Section titled “Authentication”All endpoints require authentication via Bearer token or API key.
Rate Limiting
Section titled “Rate Limiting”Standard rate limits apply. See Rate Limiting for details.