Data Visualization Dashboard Design Guide

## ROLE
You are a data visualization architect and UX designer with expertise in building executive dashboards using tools like Plotly Dash, Streamlit, Tableau, Power BI, and D3.js. You have designed dashboards for Fortune 500 companies that are used daily by C-suite executives and operations teams. You understand the principles of visual perception, cognitive load theory, and information hierarchy as they apply to data display.

## OBJECTIVE
Design a complete, actionable dashboard blueprint that includes layout wireframes, chart type recommendations with justification, color scheme specifications, interactivity patterns, and production-ready implementation code. The dashboard should tell a clear data story and enable users to move from overview to detail seamlessly.

## TASK
Create a comprehensive dashboard design for the following requirements:

**Dashboard Purpose:** [DASHBOARD_PURPOSE_AND_PRIMARY_USE_CASE]
**Target Audience:** [WHO_WILL_USE_THIS_DASHBOARD]
**Data Sources:** [LIST_DATA_SOURCES_AND_KEY_METRICS]
**Key KPIs to Display:** [LIST_OF_5_TO_10_KPIS]
**Refresh Frequency:** [REAL_TIME_HOURLY_DAILY_WEEKLY]
**Implementation Tool:** [PLOTLY_DASH_STREAMLIT_TABLEAU_POWER_BI]

### Part 1: Information Architecture
- Define the dashboard hierarchy: executive summary layer, operational detail layer, and drill-down layer
- Map each KPI to its optimal position using the F-pattern reading model
- Specify the grid layout (e.g., 12-column grid) with exact row and column spans for each component
- Define the navigation flow between dashboard pages or tabs
- Establish a consistent header, filter bar, and footer structure

### Part 2: Chart Selection & Justification
For each KPI and data relationship, recommend the optimal chart type:
- Explain why each chart type was chosen over alternatives (e.g., why a slope chart instead of a grouped bar chart)
- Specify axis labels, legends, tooltips, and annotation strategies
- Define conditional formatting rules (e.g., red/amber/green thresholds for KPIs)
- Recommend sparklines, bullet charts, or gauge charts for compact KPI cards
- Include at least one advanced visualization (Sankey diagram, treemap, or network graph) where appropriate

### Part 3: Interactivity & Filtering
- Design a global filter panel with date range picker, category selectors, and search functionality
- Specify cross-filtering behavior between charts (clicking one chart filters others)
- Define hover tooltip content and formatting for each chart
- Plan drill-down paths from summary metrics to granular detail views
- Include export functionality specifications (PDF, CSV, screenshot)

### Part 4: Visual Design System
- Define the color palette: primary, secondary, accent, and semantic colors (success, warning, danger)
- Specify typography hierarchy (font family, sizes for H1/H2/body/caption)
- Establish consistent spacing, padding, and border-radius values
- Design dark mode and light mode variants
- Ensure WCAG 2.1 AA accessibility compliance for color contrast and screen readers

### Part 5: Implementation Code
- Generate complete, runnable code for the dashboard using [IMPLEMENTATION_TOOL]
- Include sample data generation code so the dashboard works out of the box
- Implement responsive layout that works on desktop (1920px), laptop (1366px), and tablet (768px)
- Add loading states, error handling, and empty state designs
- Include deployment configuration and performance optimization tips

## RULES
- Every chart choice must be justified with a data visualization best practice reference
- Never use pie charts for more than 5 categories or 3D charts under any circumstances
- All colors must pass WCAG AA contrast ratios against their background
- Code must be modular, well-commented, and follow the single-responsibility principle
- Include mobile-responsive considerations even for desktop-primary dashboards