data-product-operator

3 diagnostic questions for evaluating data product markets through the arbitrage gap lens. Identifies whether your data product sits on a durable or closing advantage. Use when assessing data product positioning, evaluating market risk, or when someone asks "is AI going to replace this?" or "what's our moat?"

Convert dashboard requests into decision specifications. The missing layer between "build me a dashboard" and "help me decide." Use when receiving dashboard requests, reviewing analytics backlogs, prioritizing data team work, or when someone asks "what dashboard do you need?" Apply this BEFORE building anything.

Decision tree exploration for data product plans. Interview relentlessly about schema decisions, consumer contracts, quality SLAs, and delivery choices. Use when planning a data product, designing a schema, choosing a delivery method, or when someone asks "grill me on this data product" or "what am I missing in this design?"

Discover what internal data consumers actually need. Adapted Mom Test and JTBD for data teams. Use when conducting user research, interviewing stakeholders, gathering consumer requirements, running discovery sessions, or when someone asks "what do they need?" or "how do I figure out what to build?"

First-principles reasoning for data product decisions. Frames problems as data products, not dashboards or pipelines. Use when evaluating data product strategy, making build-vs-buy decisions, scoping data product features, assessing product-market fit for data offerings, or when someone asks "should we build this data product?"

Score whether a data product idea is worth building before committing resources. Validation scorecard, experiment design, and go/kill decisions. Use when evaluating feasibility, making go/no-go decisions, validating demand, sizing bets, or when someone asks "is this worth building?" or "should we invest in this?"

Position data teams as strategic partners, not order-takers. The organizational "why" behind doing discovery work. Use when discussing team positioning, value exchange, demand shaping, escaping the order-taker trap, or when someone asks "how do we stop being order-takers?" or "how does the data team become strategic?"

Convert raw discovery notes into structured insights using atomic research methods adapted for data products. Use when synthesizing findings, reviewing evidence, summarizing research, writing problem briefs, or when someone asks "what did we learn?" or "what does the evidence say?"

Dimensional modeling and schema design for data products. Star schema patterns, slowly changing dimensions, denormalization decisions, and architecture decision records. Use when designing data models, reviewing schema designs, choosing between normalization strategies, or when someone asks "how should I model this data?" or "should I denormalize?" For OMOP CDM patterns specifically, see healthcare-data-domain.

Automated data quality checks for pipelines. Testing pyramids, dbt test patterns, data contracts, circuit breakers, and monitoring. Use when implementing data quality checks, writing dbt tests, defining data contracts, setting up pipeline validation, building automated quality monitoring, or when someone asks "how do I test my data pipeline?" For quality scoring methodology (the 5-dimension rubric), see data-quality-assessment.

Data presentation and storytelling for data product operators. Narrative structures, chart selection, headline formulas, and anti-patterns. Use when presenting data to stakeholders, building a data presentation, writing an executive summary of findings, telling a data story, or making a case with data. For stakeholder alignment process, see stakeholder-alignment. For metric definitions, see metrics-definition.

Healthcare data domain context covering FHIR, HL7, OMOP CDM, real-world evidence, and clinical terminology systems. Use when working on clinical data pipelines, EHR integrations, claims data products, HIPAA-governed data, OMOP transformations, or when the conversation involves PHI, ICD-10, SNOMED, CPT, LOINC, or RxNorm. Skip this skill for non-healthcare data products.

Precise metric definitions for data products. Outcome metric trees, naming conventions, grain specification, and the "what does this number mean?" problem. Use when defining KPIs, writing metric specifications, resolving conflicting metric definitions, building a metrics catalog, or when someone asks "how should we measure success?" or "why don't these numbers match?"

Team structure and operating rhythm for data product teams. Product squads, Shape Up 6-week cycles, handoff contracts, and role assignments by lifecycle stage. Use when organizing a data team, planning data product development cycles, defining roles and responsibilities, improving team handoffs, or when someone asks "how should we structure our data team?" or "why do we keep losing context between discovery and delivery?"

Systematic data quality evaluation covering completeness, accuracy, timeliness, consistency, and validity. Use when assessing data pipelines, reviewing data product quality, auditing data sources, defining quality SLAs, building data quality monitors, or when someone asks "is this data trustworthy?" or "how do we measure data quality?"

Ethical data risk evaluation, bias testing protocols, and governance practices for data products. Use when evaluating ML/AI features for fairness, designing bias testing protocols, planning phased rollouts for high-risk changes, reviewing data governance practices, or when someone asks "could this model be biased?" or "how do we ship AI features responsibly?" For HIPAA-specific guidance, see healthcare-data-domain.

Translates between technical data teams and business stakeholders. Use when preparing stakeholder updates, translating technical data work for executives, shaping vague business requests into buildable specs, navigating competing priorities across data consumers, or when someone asks "how do I explain this to my VP?" or "the business team wants X but that's not how data works."