Open Source Data Evaluation Framework

Overview

A systematic approach for evaluating open source datasets for enterprise AI enrichment. Balances the value of external data against legal risk and compliance burden.

Why This Matters

Open source datasets multiply the value of proprietary data by providing:

• Taxonomies and classification systems
• Entity disambiguation (linking “IBM” to “International Business Machines”)
• Domain knowledge structures
• Relationship schemas

But “open source” doesn’t mean “no strings attached.” License terms vary dramatically.

The Evaluation Process

Step 1: Identify License

Look for explicit, machine-readable license terms. If licensing is ambiguous or unspecified, exclude the dataset. Common licenses to look for:

• Creative Commons variants (CC0, CC BY, CC BY-SA)
• Open Data Commons (PDDL, ODC-By, ODbL)
• Software licenses applied to data (Apache, BSD, MIT)
• Government open data licenses

Step 2: Classify by Tier

Tier	Licenses	Action
1 - Public Domain	CC0, US Gov, PDDL	Deploy immediately
2 - Attribution	CC BY, Apache, BSD	Establish attribution workflow
3 - ShareAlike	CC BY-SA, ODbL	Get legal guidance first
Excluded	Non-commercial, ambiguous	Do not use

Step 3: Assess Risk Factors

For each dataset, evaluate:

• License clarity: Are terms explicit? Machine-readable?
• Attribution requirements: What notices are needed? Where?
• Derivative work implications: Does your use case trigger ShareAlike?
• Commercial use: Explicitly permitted? (Exclude if restricted)
• Update frequency: Will you need to track license changes?

Step 4: Document Provenance

For approved datasets, capture:

• Source URL and access date
• License identifier and version
• Attribution text (exact wording required)
• Version/release identifier
• Refresh schedule

Step 5: Establish Ongoing Governance

• Quarterly review of license terms (they can change)
• Version control for dataset updates
• Audit trail for compliance demonstration
• Clear ownership of attribution maintenance

Architecture Principle

Maintain separation between open source reference data and proprietary business data:

• Dedicated schemas for external datasets
• Clear lineage tracking
• Isolation for governance purposes
• Ability to remove datasets if license changes

What to Exclude

Remove from consideration any dataset with:

• Non-commercial restrictions
• No explicit license
• Viral copyleft without clear derivative work definition
• Discontinued or unsupported status
• API-only access requiring commercial terms

Example Dataset Categories

Lowest risk (Tier 1):

• U.S. Government labor data (BLS, SOC)
• Wikidata (CC0)
• International standards (ISCO-08)

Moderate effort (Tier 2):

• O*NET (CC BY 4.0)
• ESCO skills taxonomy (CC BY 4.0)
• Technical ontologies (Apache/BSD)

Requires assessment (Tier 3):

• DBpedia, YAGO (CC BY-SA)
• ConceptNet (CC BY-SA)

Related: 04-molecule—reference-data-multiplier, 04-atom—data-governance, 04-atom—license-tier-framework, 06-atom—entity-linking