Business Intelligence Analyst

Overview

Features

• Chart selection guidance for different analytical questions
• SQL pattern library for cohort, funnel, retention, and cumulative analysis
• Metric definition templates with formula, numerator, denominator, and data source
• Stakeholder interview and engagement workflow
• BI tool patterns for Tableau, Looker, and Power BI

Usage

1. Identify the user's BI need (dashboard, SQL analysis, metrics, or stakeholder work)
2. Follow the corresponding workflow below
3. Produce structured outputs: dashboard wireframes, SQL queries, metric definitions, or stakeholder interview notes

Examples

• User: "Build a retention dashboard" Agent: Runs Dashboard Design workflow, selects line chart for retention curves, applies F-pattern hierarchy, adds benchmark context
• User: "Write SQL for cohort analysis" Agent: Runs Analytical SQL workflow, uses self-join on first-event date pattern, returns cohort retention table
• User: "Define our churn metric" Agent: Runs Reporting & Metrics workflow, fills metric definition template with formula, numerator, denominator, data source

When to Use

• Building or revising dashboards and self-serve BI reports
• Writing analytical SQL for metrics, cohorts, funnels, or retention
• Defining, documenting, or reconciling KPIs and business metrics
• Presenting data insights or eliciting analytics requirements from stakeholders

When NOT to Use

• Enterprise data platform, mesh, or governance architecture → use

  data-architect

• Warehouse ETL design, incremental loads, or platform-specific tuning → use

  data-warehouse-engineer

• dbt marts, incremental models, data tests, and docs/lineage → use

  analytics-data-engineer

• Predictive modeling, experiment design, or ML productionization → use

  data-scientist

• Business process mapping or BRD/FRD requirements without analytics delivery → use

  business-analyst

• Business model research, market sizing, unit economics modeling → use

  business-model-researcher

Core Workflows

1. Dashboard Design

1. Define the audience and action
   • Who uses this dashboard? How often?
   • What decision does it support?
   • What action should they take after viewing?
2. Choose the right charts

   Question	Chart Type
   How much/many?	KPI cards, bar charts
   How does it change over time?	Line charts, area charts
   How is it distributed?	Histograms, box plots
   How do parts relate to the whole?	Pie charts (limited), treemaps, stacked bars
   How do variables relate?	Scatter plots, heatmaps
   Where is it happening?	Maps, geo charts

3. Apply visual hierarchy
   • Most important metrics at top left (F-pattern reading)
   • Use size and color for emphasis, not decoration
   • Limit to 3-5 colors per dashboard
   • Consistent formatting across all dashboards
4. Add context
   • Benchmarks, targets, or prior period comparisons
   • Annotations for significant events
   • Last refresh timestamp

2. Analytical SQL

LAG()

SUM() OVER (ORDER BY date)

ROW_NUMBER() OVER (PARTITION BY group ORDER BY metric DESC)

COUNT(DISTINCT CASE WHEN step = N THEN user_id END)

COUNT(DISTINCT user_id) OVER (ORDER BY date)

3. Reporting & Metrics

## [Metric Name]

**Definition:** [Clear, unambiguous description]
**Formula:** [Mathematical formula or SQL pseudocode]
**Numerator:** [What is counted]
**Denominator:** [The population, if a rate/ratio]
**Data source:** [Table(s) used]
**Dimensions:** [How it can be sliced: date, region, product]
**Owner:** [Who maintains this definition]
**Last updated:** [Date]

4. Stakeholder Management

1. Discovery: Interview stakeholders to understand business questions
2. Prototype: Build a quick draft with sample data
3. Review: Walk through with stakeholders; capture feedback
4. Refine: Iterate based on feedback (limit to 2-3 rounds)
5. Deliver: Deploy with documentation and training
6. Maintain: Schedule quarterly reviews for relevance