Knowledge Use Cases

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Department Operating Guides (Sales, Marketing, Support, Finance)

• Outcome: The AI understands how each department works and frames analysis in the right operating context.
• Include: Org structure and responsibilities; core processes and handoffs; SLAs; cadences (pipeline reviews, forecast calls); definitions of success; common edge cases.
• Link to: Department/team nodes; key models; core metrics and dimensions; relevant entities (e.g., Opportunity, Ticket, Invoice).
• Title examples: Sales Operations Guide, Marketing Analytics Operating Model.

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Business Glossary and Taxonomy

• Outcome: Consistent terminology and fewer misunderstandings across teams.
• Include: Canonical terms; synonyms and forbidden terms; calculation notes; naming conventions; examples and counterexamples for tricky concepts.
• Link to: Models, metrics, and dimensions the terms govern.
• Title examples: Enterprise Segment Glossary, Attribution Taxonomy.

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Metric Interpretation and Decision Rubrics

• Outcome: Analyses read metrics the way your business does and recommend actions consistent with policy.
• Include: Metric purpose; leading/lagging relationships; how to read changes; expected ranges and seasonality; common pitfalls; decision criteria and next-best- actions.
• Link to: Metric nodes and the dimensions used to break them down.
• Title examples: ARR Interpretation Guide, Activation Rate Decision Rubric.

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Data Lineage, Quality, and Ownership

• Outcome: The AI respects data limitations and routes questions to the right sources.
• Include: Source systems; refresh cadences; known gaps/quirks; authoritative vs. derived datasets; owner contacts and escalation process.
• Link to: Models and pipelines; metrics that depend on them; data owner/team nodes.
• Title examples: Revenue Model Lineage & Quality Notes, Attribution Data Owner Guide.

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Strategic Initiatives and Quarterly Focus

• Outcome: Business Reviews and analyses prioritize current strategic goals.
• Include: Objectives and hypotheses; guardrails; success measures; relevant time windows; key stakeholders and dependencies.
• Link to: Project/initiative nodes; teams; impacted metrics; regions/categories.
• Title examples: Q1 Growth Initiatives, Cost Optimization Program Overview.

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Customer and Segment Definitions

• Outcome: Customer-facing analyses reflect real segmentation and account context.
• Include: Segmentation logic; named segment lists; strategic customers; lifecycle stages and triggers; notable exceptions.
• Link to: Customer/Account entities; segment dimensions; revenue/retention metrics.
• Title examples: Enterprise Segmentation Framework, Strategic Accounts Playbook.

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Pricing, Discounts, and Policy Constraints

• Outcome: Analyses interpret revenue and conversion with the right pricing context.
• Include: Discount policy; approval thresholds; common exceptions; how pricing impacts key metrics; reporting caveats.
• Link to: Revenue metrics (ARR, Bookings); deal/opportunity entities; Finance team.
• Title examples: Discount Policy Guide, Pricing Context for Revenue Analysis.

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Seasonality and Event Calendars

• Outcome: Variance is attributed correctly given seasonal effects and events.
• Include: Calendars (holidays, events, campaigns); expected uplift/drag; blackout periods; known interactions (e.g., weather, geo-specific patterns).
• Link to: Region, Category, and Campaign nodes; affected metrics and dimensions.
• Title examples: Retail Holiday Context, Event & Seasonality Calendar.

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Do vs Avoid

• Do: Make titles and descriptions explicit about scope and audience
• Do: Link to the exact nodes users query so the right guidance is referenced
• Avoid: Over-linking high-level nodes that pull unrelated context
• Avoid: Vague titles like “Marketing Guide” without scope or ownership