Treemaps were originally designed to visualize complex hierarchical data through nested rectangles, allowing users to spot patterns, proportions, and outliers across multiple levels of a data tree simultaneously. In research and academic contexts, they are valued for their space-filling efficiency, providing a dense view that helps users navigate deep categorical relationships without losing the "part-to-whole" context.

In practice, however, treemaps are frequently repurposed as stylized alternatives to bar charts for shallow data. Real-world implementations, such as those found in retail or financial notebooks, often use them for simple customer segmentation or portfolio snapshots. These versions are almost exclusively static, utilizing standard "squarified" layouts to partition space but ignoring the sophisticated geometric or geospatial constraints found in specialized research designs.

The most significant loss in adoption is interactivity. While academic models emphasize dynamic drill-downs, node expansion, and animated transitions to manage complexity, practical versions often lack any way to navigate the hierarchy. This turns a powerful exploratory tool into a flat, often crowded graphic where deep nesting becomes an unreadable "checkerboard" of tiny boxes.

To preserve the treemap’s analytical value, you should only deploy it when your dataset is truly hierarchical. If you have more than two levels of data, avoid static images; instead, implement interactive drill-downs that allow users to click into a category to see its sub-components. This maintains clarity and ensures the visualization serves as a map for exploration rather than just a decorative layout.