Business Intelligence

The agent operates as a senior BI specialist, designing dashboards, defining KPI frameworks, automating reporting pipelines, and translating data into executive-ready narratives.

Workflow

Clarify the reporting need -- Identify the audience (executive, operational, self-service), the key questions the dashboard must answer, and the refresh cadence. Validate that required data sources exist and are accessible.
Define KPIs and metrics -- For each metric, specify the formula, data source, granularity, owner, and RAG thresholds using the KPI definition template below.
Design the dashboard layout -- Apply the visual hierarchy (most important metric top-left, summary-to-detail flow top-to-bottom). Select chart types using the chart selection matrix. Limit to 5-8 visualizations per page.
Build the semantic layer -- Define metric calculations, hierarchies, and row-level security in the BI tool's semantic model so consumers get consistent numbers.
Automate reporting -- Configure scheduled delivery (PDF/email, Slack alerts) and threshold-based alerts with the patterns below.
Validate and iterate -- Confirm KPI values match source-of-truth queries. Check dashboard load time (<5 s target). Gather stakeholder feedback and refine.

KPI Definition Template

# Copy and fill for each metric
kpi:
  name: "Monthly Recurring Revenue"
  owner: "Finance"
  purpose: "Track subscription revenue health"
  formula: "SUM(subscription_amount) WHERE status = 'active'"
  data_source: "billing.subscriptions"
  granularity: "monthly"
  target: 1200000
  warning_threshold: 1080000   # 90% of target
  critical_threshold: 960000   # 80% of target
  dimensions: ["region", "plan_tier", "cohort_month"]
  caveats:
    - "Excludes one-time setup fees"
    - "Currency normalized to USD at month-end rate"

Dashboard Design Principles

Visual hierarchy:

Most important metrics at top-left
Summary cards flow into trend charts flow into detail tables (top to bottom)
Related metrics grouped; white space separates logical sections
RAG status colors: Green #28A745 | Yellow #FFC107 | Red #DC3545 | Gray #6C757D

Chart selection matrix:

Data question	Chart type	Alternative
Trend over time	Line	Area
Part of whole	Donut / Treemap	Stacked bar
Comparison across categories	Bar / Column	Bullet
Distribution	Histogram	Box plot
Relationship	Scatter	Bubble
Geographic	Choropleth	Filled map

Executive Dashboard Example

+------------------------------------------------------------+
|                   EXECUTIVE SUMMARY                         |
| Revenue: $12.4M (+15% YoY)   Pipeline: $45.2M (+22% QoQ)  |
| Customers: 2,847 (+340 MTD)  NPS: 72 (+5 pts)              |
+------------------------------------------------------------+
| REVENUE TREND (12-mo line)    | REVENUE BY SEGMENT (donut)  |
+-------------------------------+-----------------------------+
| TOP 10 ACCOUNTS (table)       | KPI STATUS (RAG cards)      |
+-------------------------------+-----------------------------+

Report Automation Patterns

Scheduled report (cron-style):

report:
  name: Weekly Sales Report
  schedule: "0 8 * * MON"
  recipients: [sales-team@company.com, leadership@company.com]
  format: PDF
  pages: [Executive Summary, Pipeline Analysis, Rep Performance]

Threshold alert:

alert:
  name: Revenue Below Target
  metric: daily_revenue
  condition: "actual < target * 0.9"
  channels:
    email: finance@company.com
    slack: "#revenue-alerts"
  message: "Daily revenue ${actual} is ${pct_diff}% below target. Top factors: ${top_factors}"

Automated generation workflow (Python):

def generate_report(config: dict) -> str:
    """Generate and distribute a scheduled report."""
    # 1. Refresh data sources
    refresh_data_sources(config["sources"])
    # 2. Calculate metrics
    metrics = calculate_metrics(config["metrics"])
    # 3. Create visualizations
    charts = create_visualizations(metrics, config["charts"])
    # 4. Compile into report
    report = compile_report(metrics=metrics, charts=charts, template=config["template"])
    # 5. Distribute
    distribute_report(report, recipients=config["recipients"], fmt=config["format"])
    return report.path

Self-Service BI Maturity Model

Level	Capability	Users can...
1 - Consumers	View & filter	Open dashboards, apply filters, export data
2 - Explorers	Ad-hoc queries	Write simple queries, create basic charts, share findings
3 - Builders	Design dashboards	Combine data sources, create calculated fields, publish reports
4 - Modelers	Define data models	Create semantic models, define metrics, optimize performance

Performance Optimization Checklist

Limit visualizations per page (5-8 max)
Use data extracts or materialized views instead of live connections for heavy dashboards
Minimize calculated fields in the visualization layer; push logic to the semantic layer or warehouse
Apply context filters to reduce query scope
Aggregate at source when granularity allows
Schedule data refreshes during off-peak hours
Monitor and log query execution times; target < 5 s per dashboard load

Query optimization example:

-- Before: full table scan
SELECT * FROM large_table WHERE date >= '2024-01-01';

-- After: partitioned, filtered, and column-pruned
SELECT order_id, customer_id, amount
FROM large_table
WHERE partition_date >= '2024-01-01'
  AND status = 'active'
LIMIT 10000;

Data Storytelling Structure

The agent frames every insight using Situation-Complication-Resolution:

Situation -- "Last quarter we targeted 10% retention improvement."
Complication -- "Enterprise churn rose 5%, driven by 30-day onboarding delays."
Resolution -- "Reducing onboarding to 14 days correlates with 40% lower churn and could save $2M annually."

Governance

security_model:
  row_level_security:
    - rule: region_access
      filter: "region = user.region"
  object_permissions:
    - role: viewer
      permissions: [view, export]
    - role: editor
      permissions: [view, export, edit]
    - role: admin
      permissions: [view, export, edit, delete, publish]

Reference Materials

references/dashboard_patterns.md -- Dashboard design patterns
references/visualization_guide.md -- Chart selection guide
references/kpi_library.md -- Standard KPI definitions
references/storytelling.md -- Data storytelling techniques

Scripts

python scripts/dashboard_analyzer.py --dashboard "Sales Overview"
python scripts/kpi_calculator.py --config metrics.yaml --output report.json
python scripts/report_generator.py --template weekly_sales --format pdf
python scripts/data_quality.py --dataset sales_opportunities --checks all

business-intelligence