Data Source Classification Framework

A Data Source Classification Framework is a data management framework that provides systematic categorization of data based on origin, processing state, and intended use.

• AKA: Data Taxonomy, Data Origin Taxonomy, Data Source Taxonomy, Data Classification System, Data Categorization Framework.
• Context:
  • It can typically distinguish between Organic Datasets and Proxy Datasets sources.
  • It can typically support Data Governance Policys through classification rules.
  • It can often enable Appropriate Data Usage based on data sensitivity levels.
  • It can often guide Data Pipeline Design through source characteristics.
  • It can often facilitate Compliance Management for regulatory requirements.
  • It can often integrate with Data Catalog Systems for metadata management.
  • It can range from being a Simple Data Source Classification Framework to being a Complex Data Source Classification Framework, depending on its taxonomy depth.
  • It can range from being a Binary Data Source Classification Framework to being a Multi-Level Data Source Classification Framework, depending on its category count.
  • It can range from being a Static Data Source Classification Framework to being a Dynamic Data Source Classification Framework, depending on its adaptation capability.
  • It can range from being a Manual Data Source Classification Framework to being an Automated Data Source Classification Framework, depending on its classification method.
  • ...
• Examples:
  • Production vs Non-Production Classifications, such as:
    • Organic vs Proxy Data Classification for data quality assessment.
    • Live vs Test Data Classification for environment management.
  • Privacy-Based Classifications, such as:
    • PII vs Non-PII Classification for privacy compliance.
    • Sensitive vs Public Classification for access control.
  • Origin-Based Classifications, such as:
    • Internal vs External Data Classification for source management.
    • First-Party vs Third-Party Classification for vendor data management.
  • ...
• Counter-Examples:
  • Data Format Classification, which categorizes by structure rather than source.
  • Data Quality Framework, which assesses quality rather than origin.
  • Data Schema Definition, which defines structure rather than classification.
• See: Data Management Framework, Organic Dataset, Proxy Dataset, Data Governance Policy, Data Catalog System, Vendor Dataset, Data Provenance Tracking System, Dataset Taxonomy.

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