N10-5: AI Data Asset Evaluation
Assessing the quality, provenance, and defensibility of training data during due diligence.
🎯 What You'll Learn
- ✓ Evaluate data quality
- ✓ Assess legal data provenance
- ✓ Calculate data replacement costs
- ✓ Identify data moat durability
Lesson 1: Data Quality Assessment Framework
Training data quality determines model quality. Evaluate across 5 dimensions: Accuracy (are labels correct?), Completeness (does the dataset cover edge cases?), Freshness (when was it last updated?), Consistency (are labeling standards uniform?), and Distribution (does it represent the real-world distribution?).
Sample 1,000 labels and independently verify. Target: >95% accuracy.
Does the dataset include rare but important scenarios?
Does the training data distribution match production data?
Design a data quality audit for a target AI company. Define sampling methodology and acceptance criteria for each dimension.
Lesson 2: Data Provenance & Legal Risk
Where the training data came from determines legal risk. Web-scraped data may violate copyright. User-generated data may violate privacy agreements. Licensed data may have usage restrictions that limit the model's commercial use. A clean data provenance chain is essential for diligence.
Was training data scraped from copyrighted sources without license?
Does user-generated training data comply with the company's privacy policy?
Licensed datasets may restrict commercial use, redistribution, or derivative works.
Audit the data provenance chain for a target AI company. Identify legal risks in each data source category.
Lesson 3: Data Moat Durability Assessment
A data moat erodes if: (1) the data can be independently collected by competitors (public data), (2) the data becomes stale and must be continuously refreshed, or (3) the data advantage is temporary (first-mover data advantage that competitors can replicate). The strongest data moats are proprietary, continuously growing, and legally defensible.
Can a competitor build an equivalent dataset from scratch?
Does the dataset grow automatically through product usage?
Can the company legally prevent competitors from accessing or replicating the data?
Score a target company's data moat on replicability, growth rate, and legal defensibility. Overall moat grade: strong/moderate/weak.
Continue Learning: Track 10 — AI Due Diligence
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Module Syllabus
Lesson 1: Lesson 1: Data Quality Assessment Framework
Training data quality determines model quality. Evaluate across 5 dimensions: Accuracy (are labels correct?), Completeness (does the dataset cover edge cases?), Freshness (when was it last updated?), Consistency (are labeling standards uniform?), and Distribution (does it represent the real-world distribution?).
Lesson 2: Lesson 2: Data Provenance & Legal Risk
Where the training data came from determines legal risk. Web-scraped data may violate copyright. User-generated data may violate privacy agreements. Licensed data may have usage restrictions that limit the model's commercial use. A clean data provenance chain is essential for diligence.
Lesson 3: Lesson 3: Data Moat Durability Assessment
A data moat erodes if: (1) the data can be independently collected by competitors (public data), (2) the data becomes stale and must be continuously refreshed, or (3) the data advantage is temporary (first-mover data advantage that competitors can replicate). The strongest data moats are proprietary, continuously growing, and legally defensible.