Hexagons are a type of spatial grid used in location intelligence that offer several advantages over irregular zoning, including tessellation and well-structured geometry. They can be particularly useful for representing curves of geographic features and gradual spatial changes. However, they also have some limitations, such as potential loss of raw data and precision issues when aggregating data. CARTO offers a global hierarchical grid system called H3 that provides a workaround to these problems and enables efficient spatial operations. H3 grids are well-suited for machine learning and modeling flows and movement, making them ideal for users with specific use cases. By using hexagons and H3 grids, users can create more accurate and informative maps that tell compelling stories about their data.