[DATA_STRUCTURE] = Format and organization of your data [VISUALIZATION_TYPE] = Chart or graph format desired [INTERACTIVE_ELEMENTS] = User interaction capabilities [COLOR_SCHEME] = Visual style preferences [INTEGRATION_CONTEXT] = Where visualization will be used Step 1 → Data Requirements Analysis Define the data preparation needs based on [DATA_STRUCTURE]: Required data format and preprocessing steps Key variables and relationships to highlight Data aggregation or transformation requirements Scale and dimension considerations Time-series or categorical handling specifications Step 2 → Visualization Architecture Selection Design the optimal visualization approach using [VISUALIZATION_TYPE]: Chart or graph type with justification Layout and component organization Axis configuration and scaling methodology Legend and label strategy Annotation and reference line requirements Step 3 → Interactivity Implementation Develop interactive elements based on [INTERACTIVE_ELEMENTS]: Hover and tooltip functionality Click actions and filtered views Zoom and pan capabilities Animation timing and effects State management for user interactions Step 4 → Visual Design Integration Create a cohesive visual style incorporating [COLOR_SCHEME]: Color palette implementation with accessibility considerations Typography selection and hierarchy Border, background, and grid styling Visual encoding consistency Responsive behavior for different screen sizes Step 5 → Integration & Deployment Guidance Provide implementation instructions for [INTEGRATION_CONTEXT]: Complete, well-commented source code Library dependencies and version requirements Performance optimization suggestions Cross-browser compatibility notes Implementation steps with code examples Pro Tip: When creating data visualizations, follow the "5-second rule" - a viewer should be able to understand the main insight within 5 seconds. Prioritize clear titles, simplified data presentation, and visual hierarchy over complex features that might obscure the core message.