business-intelligence

---
name: business-intelligence
description: >
license: MIT
---

# 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.

## Clarify First

Before designing the dashboard, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **Audience** — executive, operational, or self-service (sets the layout, altitude, and metric count per page)
- [ ] **Key questions + refresh cadence** — what decisions the dashboard drives and how fresh the data must be (scopes the metrics and the live-vs-extract choice)
- [ ] **KPI definitions** — formula, data source, owner, and RAG thresholds per metric (these are the exact fields the KPI template and `metric_validator.py` require)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

## Workflow

1. **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.
2. **Define KPIs and metrics** -- For each metric, specify the formula, data source, granularity, owner, and RAG thresholds using the KPI definition template below.
3. **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.
4. **Build the semantic layer** -- Define metric calculations, hierarchies, and row-level security in the BI tool's semantic model so consumers get consistent numbers.
5. **Automate reporting** -- Configure scheduled delivery (PDF/email, Slack alerts) and threshold-based alerts with the patterns below.
6. **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

```yaml
# 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:**
1. Most important metrics at top-left
2. Summary cards flow into trend charts flow into detail tables (top to bottom)
3. Related metrics grouped; white space separates logical sections
4. 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):**
```yaml
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:**
```yaml
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):**
```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:**
```sql
-- 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:

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

## Governance

```yaml
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

```bash
python scripts/kpi_tracker.py --definitions kpis.json --data sales.csv
python scripts/kpi_tracker.py --definitions kpis.json --data sales.csv --json
python scripts/dashboard_spec_generator.py --definitions kpis.json --title "Sales Dashboard"
python scripts/dashboard_spec_generator.py --definitions kpis.json --layout 3-column --json
python scripts/metric_validator.py --definitions metrics.json --strict
python scripts/metric_validator.py --definitions metrics.json --json
```

## Tool Reference

| Tool | Purpose | Key Flags |
|------|---------|-----------|
| `kpi_tracker.py` | Calculate KPIs from data against targets; report RAG status and variance | `--definitions <json>`, `--data <csv/json>`, `--json` |
| `dashboard_spec_generator.py` | Generate dashboard layout specs (chart types, positions, filters) from KPI definitions | `--definitions <json>`, `--title`, `--layout 2-column/3-column`, `--json` |
| `metric_validator.py` | Validate metric definitions for completeness, naming, threshold logic, and consistency | `--definitions <json>`, `--strict`, `--json` |

## Troubleshooting

| Problem | Likely Cause | Resolution |
|---------|-------------|------------|
| Dashboard loads slowly (> 5 s) | Too many visualizations or live-connection queries hitting raw tables | Reduce widgets to 5-8 per page; switch to extracts or materialized views for heavy dashboards |
| KPI values differ between dashboard and source query | Dashboard applies additional filters, currency conversion, or calculated fields not in the semantic layer | Centralize all metric logic in the semantic layer; remove dashboard-level computed fields |
| RAG thresholds trigger false alerts | Warning/critical percentages are miscalibrated for seasonal patterns | Adjust thresholds per season or use rolling baselines; validate with `metric_validator.py --strict` |
| Stakeholders ignore dashboards | Dashboard answers the wrong questions or lacks actionable context | Redesign using the Situation-Complication-Resolution storytelling framework; add annotations and targets |
| Row-level security hides data unexpectedly | Security rules are too broad or user-role mapping is incorrect | Audit RLS rules; test with a sample user from each role; log filtered row counts |
| Scheduled report emails land in spam | Large PDF attachments or sender reputation issues | Reduce attachment size; switch to embedded links; work with IT to whitelist the sender domain |
| `metric_validator.py` reports formula-aggregation mismatch | The `formula` field (e.g., "SUM(...)") does not match the declared `aggregation` | Align the two fields; the aggregation field drives the tool while the formula documents intent |

## Success Criteria

- Dashboard load time is under 5 seconds for 95% of page views.
- KPI definitions pass `metric_validator.py --strict` with zero errors before production deployment.
- Executive dashboards follow the visual hierarchy: summary cards at top-left, trends in the middle, detail tables at the bottom.
- Every KPI has a defined owner, target, and RAG thresholds documented in the definitions file.
- Self-service BI adoption reaches Level 2 (Explorers) for at least 60% of target users within 90 days.
- Scheduled reports are delivered within 15 minutes of the configured schedule window.
- Data storytelling follows the What / So What / Now What structure with quantified impact in every insight.

## Scope & Limitations

**In scope:** Dashboard design and layout, KPI framework definition, report automation patterns, data storytelling, self-service BI enablement, row-level security configuration, and visualization best practices.

**Out of scope:** Data warehouse infrastructure, ETL/ELT pipeline development, raw data ingestion, machine learning model building, and BI tool installation or licensing.

**Limitations:** The Python tools (`kpi_tracker.py`, `dashboard_spec_generator.py`, `metric_validator.py`) operate on local JSON and CSV files only -- they do not connect to live databases or BI platforms. All scripts use the Python standard library with no external dependencies. Dashboard specifications are platform-agnostic and require manual translation to specific BI tools (Tableau, Power BI, Looker, etc.).

## Integration Points

- **Analytics Engineer** (`data-analytics/analytics-engineer`): Provides the mart models and semantic-layer metrics that dashboards consume; schema changes require dashboard updates.
- **Data Analyst** (`data-analytics/data-analyst`): Creates ad-hoc analyses that may evolve into repeatable dashboards; shares visualization standards.
- **Product Team** (`product-team/`): Defines product KPIs and user-facing analytics requirements.
- **C-Level Advisor** (`c-level-advisor/`): Executive dashboards translate strategic objectives into measurable KPIs.
- **Finance** (`finance/`): Financial KPIs (MRR, CAC, LTV) require alignment between BI dashboards and finance team definitions.