# Bi Analyst

> Use when building business intelligence dashboards, data models for BI tools, KPI reporting systems, or self-service analytics platforms. Trigger phrases: "BI dashboard", "Power BI", "Tableau dashboard", "Looker", "Metabase", "self-service analytics", "executive dashboard", "data model for BI", "semantic layer", "star schema", "KPI report", "business dashboard", "OLAP", "cube", "drill-down", "DAX", "LookML", "measures and dimensions".

- Skill: `barastrong/bi-analyst` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add barastrong/bi-analyst`
- Raw SKILL.md: https://api.skillmd.com/api/skills/barastrong/bi-analyst/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: barastrong (https://skillmd.com/u/barastrong)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/barastrong/bi-analyst

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# Business Intelligence (BI) Analyst

## Role Summary

A BI Analyst designs and maintains dashboards, reports, and data models that
give business stakeholders self-service access to key metrics. The role
connects the data warehouse to business decisions through well-designed,
trustworthy, and fast reporting layers.

## Core Responsibilities

- Design and build BI dashboards (Tableau, Power BI, Looker, Metabase)
- Build semantic/presentation layer data models (star schema, LookML, DAX)
- Define KPIs and ensure metric consistency across the organization
- Conduct requirements sessions with business stakeholders
- Maintain a single source of truth for business metrics
- Optimize query performance for dashboard responsiveness
- Train business users on self-service analytics
- Govern report access and data security/RLS

## Standard Workflow

1. **Requirements** — identify questions to answer, audience, refresh
   frequency, and drill-down needs.
2. **Metric Definition** — agree on metric definitions, business logic,
   edge cases (e.g., "what counts as an active user?").
3. **Data Model** — build or extend semantic layer (star schema fact/dim,
   LookML Explores, Power BI datasets, dbt metrics layer).
4. **Dashboard Build** — layout, chart selection, filters, drill-through.
5. **Performance Tuning** — aggregation tables, extracts, query optimization,
   caching.
6. **Review & Publish** — stakeholder review, UAT, access control, publish.
7. **Maintenance** — monitor usage, deprecate unused reports, update on
   schema changes.

## Technology Stack

| Layer | Tools |
|-------|-------|
| BI Tools | Tableau, Power BI, Looker, Metabase, Apache Superset, Grafana |
| Semantic Layer | LookML, DAX, dbt Metrics, AtScale, Cube.dev |
| Query | SQL, DAX, MDX |
| Data Warehouse | Snowflake, BigQuery, Redshift, Databricks |
| Data Modeling | Star schema, Snowflake schema, OBT, dbt |

## Best Practices

- Metric definitions in code (dbt metrics, LookML) — not in dashboard calc fields.
- Star schema for BI: wide fact tables, clean dimension tables.
- Named, documented, consistent measures — no ad-hoc inline calculations.
- Row-Level Security (RLS) for sensitive data — finance, HR, customer PII.
- One source of truth per metric — eliminate divergent reports.
- Dashboard UX: 5 charts max per view, mobile-responsive where needed.
- Published data dictionary: what each metric means, how it's calculated.
- Aggregation/extract strategies for dashboards > 10-second load time.

## Anti-Patterns to Avoid

- Duplicate metrics with slightly different definitions across dashboards.
- Embedding business logic in dashboard calculated fields (hard to audit).
- Dashboards nobody uses — audit usage, deprecate orphaned reports.
- No access control — sensitive data visible to all.
- Live query on 1B-row tables — use extracts or aggregation tables.
- Visual clutter: too many charts, colors, and filters on one screen.

## References

- `references/kpi-definition-template.md` — KPI definition standard
- `references/dashboard-design-guide.md` — BI UX best practices
- `references/star-schema-patterns.md` — dimensional modeling guide

## Expected Output Format

1. Data model (SQL/LookML/DAX) with documented metrics
2. Published dashboard with clear titles, sources, and refresh schedule
3. KPI dictionary entry for each new metric
4. Access control configuration