Certification
The Certification feature allows you to formally certify the quality status of your data sources based on defined criteria and quality metrics.
Overview
Data certification is the process of formally validating that a data source meets predefined quality standards. In QALITA Platform, certification workflows help organizations establish trust in their data by providing a structured framework for quality validation.
Certification Workflow
The certification process follows these steps:
- Define Criteria: Establish the quality criteria that a source must meet to be certified (e.g., minimum quality scores across key dimensions).
- Assess Quality: Run quality packs against the source to evaluate its quality across all relevant dimensions.
- Review Results: Review the quality metrics and recommendations produced by the assessment.
- Certify or Reject: Based on the results, certify the source as meeting quality standards or flag it for improvement.
- Monitor: Continuously monitor certified sources to ensure they maintain their quality levels.
Quality Dimensions
Certification can be based on any combination of the 11 data quality dimensions supported by the platform:
| Dimension | Description |
|---|---|
| Completeness | How complete is the dataset? |
| Accuracy | How faithfully does the data represent reality? |
| Validity | Does the data conform to defined formats and ranges? |
| Timeliness | Is the data up to date? |
| Uniqueness | Are there duplicates? |
| Consistency | Is data synchronized across systems? |
| Reasonability | Are values within reasonable bounds? |
| Schema | Is the data structure correct? |
| Version | How has the data model evolved? |
| Interoperability | Can data be shared across systems using common standards? |
| Consent | Does data usage comply with consent requirements? |
Current Status
The Certification feature is currently under active development. The certification workflow and criteria management features are being built and will be fully available in an upcoming release.