If you need to turn an address into a geographic point you need geocoding tools and will have to again depend on an external service or go through a complex process like this in Tableau.You are limited to predefined boundaries such as postal codes or states and this all depends on your data perfectly matching the naming in the BI tool.Some data types won’t render such as lines in Power BI. Users of Power BI have hit limits around 35k features and in Tableau it's closer to 65k features ( more here in a Tableau community post). There are visualization limits on the total number of features you can show on a map.The tool’s native spatial functions (like a spatial join or intersect) don’t run on the data itself they run in memory in the tool (more on this later).You often have to use a data preparation tool like Alteryx (which has its own limitations and additional cost).These are just some of the issues with doing spatial analytics in a business intelligence tool: Have you ever had to deal with the EPSG projection in data before? Welcome to geospatial analytics. You can see the problems illustrated in videos like this one and this one. This presents a number of issues when trying to move geospatial data into Tableau. In short geospatial data has added complexity compared to non-geospatial data and tools like Tableau and Power BI are built primarily for tabular data that is not geospatial in nature. Want to add a few more layers like points or demographic data to your map? You likely won’t be able to as there are data layer limits in both tools. While there are a few common data types like Shapefiles and GeoJSON there are close to 200 individual formats of geospatial data. If you need to translate data formats. Either large number of rows or features or complex geometries (take a look at the coast of Norway if you need an example).
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