```json { "scores": [ { "id": "cnv_e722c382cdc74d87", "score": 72, "tier": "regular", "rationale": "Good orientation to dataset purpose (telemetry platform), but lacks specifics on row counts, time period, or concrete findings." }, { "id": "cnv_09243b2a4fe449c5", "score": 75, "tier": "regular", "rationale": "Solid schema documentation with data types and semantic roles. Useful reference but foundational rather than insightful." }, { "id": "cnv_a4bb7762a2074dfb", "score": 85, "tier": "great", "rationale": "Concrete: 2.04M rows, 43 tables, 507 columns, May-Aug 2026. Specific table sizes and time range immediately actionable." }, { "id": "cnv_e569e6d095554ae1", "score": 78, "tier": "regular", "rationale": "Systematic column classification (178 identifiers, 124 categorical, etc.) with examples. Useful taxonomy but somewhat mechanical." }, { "id": "cnv_8e4390ff5f59406c", "score": 68, "tier": "regular", "rationale": "Shows sample rows and context, but lacks depth. Confirms structure without revealing patterns or anomalies." }, { "id": "cnv_8bd62072c5764398", "score": 82, "tier": "great", "rationale": "Identifies 50 completely empty columns and 14 with 97-99% missing. Specific, actionable data quality finding." }, { "id": "cnv_c4ed422af7d54ea4", "score": 65, "tier": "not_useful", "rationale": "Confirms no duplicates across 3 tables. Correct but low-signal; absence of duplicates is expected, not surprising." }, { "id": "cnv_057c2d45d6bb474a", "score": 72, "tier": "regular", "rationale": "Cardinality analysis (52 tags, 16 subtypes, 10 types). Useful reference but mechanical enumeration without insight." }, { "id": "cnv_8322dd8d35a2452d", "score": 80, "tier": "great", "rationale": "Detailed distributions (min/max/mean/median) for numeric columns with concrete ranges and skewness observations." }, { "id": "cnv_187e2a7c80214a30", "score": 88, "tier": "great", "rationale": "Identifies 417 quality issues across 80+ datasets with severity tiers. Specific, actionable, reveals real data problems." }, { "id": "cnv_b13d56804cd24032", "score": 79, "tier": "regular", "rationale": "Shows frequency of job phases (expansion 19K, llm 10K, etc.) and tag distribution. Useful but descriptive rather than surprising." }, { "id": "cnv_f36cc173903d4c88", "score": 35, "tier": "not_useful", "rationale": "Failed to deliver on question; only describes table structure. No actual correlation findings despite high confidence claim." }, { "id": "cnv_570e8012fdcb43e9", "score": 86, "tier": "great", "rationale": "Identifies LLM cost as key metric ($609.16 total, $0.000025-$1.70 range). Shows highest/lowest records with business context." }, { "id": "cnv_bd5ebec9bd8b407c", "score": 87, "tier": "great", "rationale": "Clear trend: 180× growth May-July, then 48% decline in August. Specific numbers, actionable for capacity planning." }, { "id": "cnv_a85d661c3102411c", "score": 84, "tier": "great", "rationale": "Identifies July 21-22 spike (47 batches, 14× normal) and secondary patterns. Concrete dates and magnitudes." }, { "id": "cnv_5eeadecf0d2a49ce", "score": 81, "tier": "great", "rationale": "Connects LLM cost to model choice (Claude Sonnet $4.97 vs Haiku $0.14) with variance analysis. Strategic insight." }, { "id": "cnv_9f08a29669424768", "score": 77, "tier": "regular", "rationale": "Identifies 3 natural layers (metadata 77%, execution 15%, intelligence 8%). Useful architecture but somewhat obvious." }, { "id": "cnv_4672e01e160f40be", "score": 70, "tier": "regular", "rationale": "Geographic data: 92.9% US, Quincy 28.4%. Specific but limited actionability; geo dimension appears minor." }, { "id": "cnv_f3e3ad700bd14945", "score": 92, "tier": "great", "rationale": "Counterintuitive: zero-LLM runs use MORE compute (41.31s) than AI-assisted runs (36.82s). Surprising, specific, investigation-worthy." }, { "id": "cnv_f20c9bb653cd4e06", "score": 58, "tier": "not_useful", "rationale": "Proposes 3 questions dataset could answer, but provides no answers or evidence. Speculative without grounding." } ] } ```