Data Versioning & Management

[levelslip]

• Description: Setup data versioning and tracking system
• Activities:
  • Implement data versioning:
    • Create versioning strategy (semantic versioning for datasets)
    • Document dataset versions and changes
    • Track data lineage (what transformations were applied)
  • Create data manifest:
    • Document dataset schema and format
    • Document data statistics (row count, column types)
    • Document data quality metrics
  • Setup version tracking:
    • Use DVC (Data Version Control) or similar
    • Track dataset changes over time
    • Create reproducibility mechanisms
  • Create data dictionary:
    • Document all fields and their meanings
    • Document data types and ranges
    • Document missing value conventions
    • Document any calculations or transformations
  • Document data provenance:
    • Track data sources
    • Document data collection methods
    • Track transformations applied
    • Document validation and quality checks
  • Setup reproducibility:
    • Ensure data versions can be recreated
    • Document all parameters and assumptions
    • Create validation tests
• Deliverables: Data versioning system, data dictionary, manifests