Designing a GUI for data-heavy applications involves several key considerations:

1. Clarity: Present complex data in a clear and concise manner.

2. Organization: Structure data into logical categories and hierarchies.

3. Visualization: Utilize charts, graphs, and other visualizations to facilitate understanding.

4. Filtering and sorting: Provide tools for users to narrow down and organize data.

5. Search functionality: Implement robust search capabilities.

6. Pagination and scrolling: Manage large datasets with pagination or infinite scrolling.

7. Data summarization: Offer summaries and overviews of key data points.

8. Customization: Allow users to tailor the GUI to their specific needs.

9. Performance optimization: Ensure the GUI remains responsive with large datasets.

10. User guidance: Provide guidance and feedback to help users navigate complex data.

11. Consistency: Establish consistent design patterns throughout the GUI.

12. Accessibility: Ensure the GUI is accessible for users with disabilities.

13. Iterative testing: Conduct user testing and refine the GUI accordingly.

14. Data storytelling: Present data in a narrative format to facilitate understanding.

15. Collaboration: Consider features for collaborative data analysis and sharing.

By addressing these factors, you can create a GUI that effectively supports users in exploring and making sense of complex data.