To balance aesthetics and readability in a data visualization GUI:

1. Prioritize clarity: Ensure data is easily readable and understandable.

2. Use color effectively: Select colors that convey meaning and are accessible.

3. Typography matters: Choose clear, legible fonts and consistent sizing.

4. Sufficient whitespace: Balance elements with sufficient whitespace.

5. Consistency: Establish a consistent design language.

6. Intuitive layout: Organize elements logically and intuitively.

7. Avoid clutter: Limit unnecessary elements and visual noise.

8. Visual hierarchy: Create a clear visual hierarchy to guide the user's attention.

9. High contrast: Ensure sufficient contrast between text, background, and visual elements.

10. Responsive design: Ensure the GUI adapts to various devices and screen sizes.

11. User testing: Validate design decisions with user testing and feedback.

12. Iteration: Refine the design based on user feedback and testing results.

Additionally, consider:

- Using pre-defined color palettes and typography styles

- Limiting the number of colors and fonts

- Using icons and graphics judiciously

- Ensuring accessibility and readability on various devices

- Using interactive elements to enhance engagement

By balancing aesthetics and readability, you can create a data visualization GUI that is both visually appealing and effective in communicating insights.