Data lineage refers to the tracking and visualization of data’s origin, movement, transformation, and usage across its lifecycle. It answers questions like: Where did this data come from? How was it changed? Who accessed it?
Understanding data lineage is critical for compliance, debugging data issues, and ensuring trust in data pipelines and reports.
Why Data Lineage Matters
- Trust: Builds transparency into how data was processed
- Compliance: Helps meet audit and privacy regulations (e.g., GDPR)
- Troubleshooting: Identifies where data errors originate
- Impact analysis: Shows downstream effects of changes to a source or schema
Key Components of Data Lineage
- Source systems: Where data originates
- Transformation steps: How it was cleaned, merged, or calculated
- Target systems: Where it ends up (e.g., dashboards, reports)
- Metadata: Contextual information like timestamp, user, and tool
Data Lineage in ClicData
- Track refresh histories and transformation steps
- Document and visualize workflows within the ETL module
- Use naming conventions and metadata for clarity
- Monitor data flows from source to dashboard