| name | business-intelligence |
| description | Expert business intelligence covering dashboard design, data visualization, reporting automation, and executive insights delivery. Use when designing dashboards, building KPI frameworks, automating scheduled reports, creating data stories for stakeholders, or optimizing BI tool performance.
|
| license | MIT + Commons Clause |
| metadata | {"version":"1.0.0","author":"borghei","category":"data-analytics","updated":"2026-03-31T00:00:00.000Z","tags":["bi","dashboards","visualization","reporting","insights"]} |
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
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
critical_threshold: 960000
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."""
refresh_data_sources(config["sources"])
metrics = calculate_metrics(config["metrics"])
charts = create_visualizations(metrics, config["charts"])
report = compile_report(metrics=metrics, charts=charts, template=config["template"])
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
Query optimization example:
SELECT * FROM large_table WHERE date >= '2024-01-01';
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/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.