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Seeing is Believing with Tableau | Part Two

Posted by Jennie Ventura on Jan 21, 2021 2:30:00 PM

In Part One of my Tableau blogs (link here!) we discussed the options for how to present, analyze and slice and dice your data in Tableau; we quickly recognized that the options are endless. No matter what tool you are using, the ability to remove the ‘noise’ from the data and highlight the useful data to look at it in a meaningful way is immensely powerful. Seeing truly is believing.

We can use Tableau to load the data from any data source such as spread sheet, .csv files or an online data repository. Once the data is in, you can easily plot it on a map. This makes for enormously powerful visualizations to illustrate that data by geography. Why a map, you might ask? It makes sense when you have a ‘spatial’ question. The map can help you understand trends or patterns in your data. This in turn makes for better informed decisions!

Here is a great example, provided by one of our senior Tableau instructors:

A business team for a large retail chain would like to visualize sales and profit data of their stores in the last 3 years. They want to analyze the data by state and postal code. The goal is to see where the sales and profits were strong and weak. A Dashboard with Sales per state, sales and profit per state and sales and profit per zip code would help the team quickly identify the strong markets and weak markets. Let’s look at some visualization options:


Visualization 1: STANDARD BAR CHART. This shows sales data by STATE. The user can move their mouse over each bar to get to the actual data.


Visualization 2: Sales data by state represented on a map. This allows you to select a state and see the sales and data and move your mouse over each state to see the actual data.


Visualization 3: This uses a map to look at sales by ZIP CODE. You can select the color-coded points on the map that corresponds to each zip code. Move your mouse over each point to the get the data.

While the bar chart is a great use of visualization and in some cases may be all you need, you can quickly see how powerful it is to see the data geographically represented. The good news? There are so many map types in Tableau: Proportional Symbol maps, Choropleth maps, Point Distribution maps, Heatmaps, Flow maps, and spider maps to name a few.

However, just because you have geographical data does not mean you should map it. There are several things to consider to be sure your data makes sense. Your data should have a geographic component – specific locations like an address or zip code and be able to show a clear distribution of the data points and how they correlate to each other. When you are analyzing large areas or do not have defined locations or points your maps will not paint a clear picture.

If you would like to learn more, please join Fastlane’s upcoming Webinar, "Getting to Know Tableau" on January 25th!

Topics: Data, Tableau, Geofence, Geofencing, Maps, Mapping

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