One of the most underrated data visualization techniques, in my experience, is network graphs combined with node/edge weighting. While everyone tends to focus on charts like bar graphs, line charts, or even heatmaps, network-based visualizations can reveal relationships and patterns that aren’t obvious in standard plots.

Why I find them underrated:

• Relationship discovery: They show how entities interact rather than just individual metrics.
• Pattern recognition: Clusters, hubs, and bottlenecks are immediately visible.
• Scalability: With proper filtering or aggregation, they can handle complex datasets with many-to-many relationships.
• Flexibility: Can be applied to social networks, citation networks, product recommendations, or even workflow dependencies.

Other underrated visualizations I’ve used include Sankey diagrams for tracking flow between categories and dimensionality reduction plots (like t-SNE or UMAP) for understanding high-dimensional data.

The key is that these visualizations often provide insight into structure and relationships, not just individual values, making them invaluable for complex or interconnected datasets.