BI Architecture: Business Intelligence Solution Design & Analytics Strategy

Generic, brand-neutral engineering capability; deep, sourced playbooks live in references/ and knowledge/. [DOC]

Generic, brand-neutral engineering capability; sourced playbooks in references//knowledge/. [DOC]

TL;DR

BI architecture defines how organizations consume data for decision-making — KPI frameworks, semantic layers, dashboard hierarchies, self-service analytics, and governance. This skill produces BI documentation that enables teams to deliver trustworthy, scalable, and accessible analytics [EXPLICIT]

When to Use

• Designing KPI frameworks and metric hierarchies for an organization
• Building semantic layers that define business metrics consistently
• Architecting dashboard hierarchies from executive to operational levels
• Enabling self-service analytics with governed data access
• Establishing visualization standards and chart selection guidelines
• Evaluating BI platform choices and migration strategies

When NOT to Use

• Data pipeline design and orchestration → use data-engineering skill
• dbt transformations and data modeling → use analytics-engineering skill
• ML model serving and prediction systems → use data-science-architecture skill
• Data quality rules and validation → use data-quality skill

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Sub-capabilities (resource map)

Deep, evidence-tagged playbooks — open the one the task needs (ICM Layer 3, on-demand). [INFERENCE]

Procedure

1. Resolve the sub-capability; open the matching references/ playbook. [EXPLICIT]
2. Apply its decision tables; pick the strategy explicitly. [EXPLICIT]
3. Validate against the Quality Criteria and tag every claim. [EXPLICIT]

Quality Criteria

• Sub-capability resolved to one playbook. [INFERENCE]
• Claims evidence-tagged. [EXPLICIT]

Contract

• Aceptación: capability resolved to its reference playbook, applied, validated, evidence-tagged. [EXPLICIT]
• Límites: · Focuses on analytics consumption, not data pipeline engineering · Does not design transformation logic (dbt models, SQL) · Does not address data quality upstream of consump. [EXPLICIT]
• Casos borde: Caso Estrategia de Manejo --- --- Startup sin BI existente Iniciar simple: una herramienta de dashboard, spreadsheet compartida para definiciones de metricas, access co. [EXPLICIT]
• Supuestos: · Data warehouse or lakehouse exists with curated data models · Business stakeholders have defined strategic objectives and KPIs · Data team exists to maintain semantic layer and g. [SUPUESTO]
• Trade-off: Decision Enables Constrains Threshold --- --- --- --- Centralized Semantic Layer Single source of truth Bottleneck on central team 50+ report consumers . [EXPLICIT]

Packet

Capas del packet, cargables bajo demanda (disciplina ICM: una capa por vez, nunca todas juntas): references/ guías de profundidad (cargar UNA por etapa) · knowledge/ cuerpo de conocimiento · prompts/ prompts listos · examples/ salida de ejemplo · agents/ subagentes del packet · assets/ recursos estáticos.