--- name: kpi-dashboard-design description: > Design and build dashboards that track key performance indicators. Select relevant metrics, visualize data effectively, and communicate insights to stakeholders. --- # KPI Dashboard Design ## Table of Contents - [Overview](#overview) - [When to Use](#when-to-use) - [Quick Start](#quick-start) - [Reference Guides](#reference-guides) - [Best Practices](#best-practices) ## Overview Effective KPI dashboards make performance visible, enable data-driven decisions, and help teams align around shared goals. ## When to Use - Creating performance measurement systems - Leadership reporting and visibility - Operational monitoring - Project progress tracking - Team performance management - Customer health monitoring - Financial reporting ## Quick Start Minimal working example: ```python # Select relevant, measurable KPIs class KPISelection: KPI_CRITERIA = { 'Relevant': 'Directly aligned with business strategy', 'Measurable': 'Can be quantified and tracked', 'Actionable': 'Team can influence the metric', 'Timely': 'Measured frequently (daily/weekly)', 'Bounded': 'Has clear target/threshold', 'Simple': 'Easy to understand' } def identify_business_goals(self): """Map goals to KPIs""" return { 'Revenue Growth': [ 'Monthly Recurring Revenue (MRR)', 'Annual Recurring Revenue (ARR)', 'Customer Lifetime Value (CLV)', 'Average Revenue Per User (ARPU)' ], 'Customer Acquisition': [ 'Customer Acquisition Cost (CAC)', 'Conversion Rate', 'Traffic to Lead Rate', // ... (see reference guides for full implementation) ``` ## Reference Guides Detailed implementations in the `references/` directory: | Guide | Contents | |---|---| | [KPI Selection Framework](references/kpi-selection-framework.md) | KPI Selection Framework | | [Dashboard Design](references/dashboard-design.md) | Dashboard Design | | [Dashboard Implementation](references/dashboard-implementation.md) | Dashboard Implementation | | [KPI Monitoring & Governance](references/kpi-monitoring-governance.md) | KPI Monitoring & Governance | ## Best Practices ### ✅ DO - Start with business goals, not data - Limit dashboards to 5-7 core metrics - Include both leading and lagging indicators - Assign clear metric ownership - Update dashboards regularly - Make drill-down available - Use visual hierarchy effectively - Test with actual users - Include context and benchmarks - Document metric definitions ### ❌ DON'T - Create dashboards without clear purpose - Include too many metrics (analysis paralysis) - Forget about data quality - Build without stakeholder input - Use confusing visualizations - Leave dashboards stale - Ignore mobile viewing experience - Skip training on dashboard usage - Create metrics no one can influence - Change metrics frequently