arbitrage-audit-data

## The 3 Questions (Data Product Context) ### Question 1: What inefficiency is this data product built on? Every data product sits on top of a gap. Name it: | Gap Type | Data Product Example | Closing Speed | |----------|---------------------|---------------| | **Knowledge asymmetry** | "Only our analysts know how to calculate this metric" | Fast — AI can learn metric definitions | | **Fragmentation** | "Data lives in 5 systems nobody has connected" | Medium — integration tools accelerating | | **Speed** | "This report takes 3 days of manual SQL" | Fast — automation and agents | | **Discipline** | "People skip the quality checks" | Medium — AI can enforce process | | **Judgment** | "Someone needs to decide which cohort definition is clinically valid" | Slow — requires domain expertise | | **Relationship** | "The client trusts our interpretation, not just the numbers" | Slow — fundamentally human | Ask: "If a competitor had the same data and unlimited AI, what would still be hard for them to replicate?" ### Question 2: How fast can AI close this gap? **Informational data products** (reports, dashboards, automated queries) face fast closure. If your data product's value is "we run the SQL so you don't have to," the clock is ticking. **Judgment data products** (cohort validation, clinical interpretation, business context) face slow closure. If your data product's value is "we know what this number means for YOUR situation," that's durable. Specific data product signals: | Signal | Gap Closing | Action | |--------|-------------|--------| | Consumer could get the same answer from ChatGPT + raw data | Fast | Migrate to judgment layer | | Consumer needs your domain expertise to interpret results | Slow | Encode and protect that expertise | | Consumer uses your output as input to another automated system | Fast | The consuming system will eventually skip you | | Consumer uses your output to make human decisions | Slow | Double down on decision context | ### Question 3: What new gap opens when this one closes? When AI closes the "generate the report" gap, the new gap is: "who decides if this report is answering the right question?" When AI closes the "connect the data sources" gap, the new gap is: "who decides what the connected data means for this specific business context?" **For data teams:** The upstream gap is always decision architecture — knowing what to measure, why, and what to do with the answer. That's where `data-team-positioning` (demand-shaper stance) lives. ## Output ```markdown # Data Product Arbitrage Audit: [Product Name] Date: [date] ## The Gap [What inefficiency this product is built on] ## Gap Type & Closing Speed [Type] — [Fast/Medium/Slow] — [rationale] ## Upstream Gap [What becomes the new bottleneck] ## Position Assessment [Closing gap → migrate. Durable gap → encode and protect.] ## Recommended Action [One specific thing] ``` Based on Nate Jones' Arbitrage Gap Framework. Generic version: [decision-architecture](https://github.com/hollandkevint/decision-architecture). --- Part of the [Data Product Operator](https://github.com/hollandkevint/data-product-operator) plugin.