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Tableau for Data Visualization

Introduction

Sometimes, a simple bar or line chart is best for the audience and the project requirements. Other times, a more complex chart is needed. Tableau can handle a wide array of charts, as shown in this Tableau Chart Catalog available on Tableau Public. 

Chart building only scratches the surface of what Tableau can do, however. Rather, the purpose of this tool is to encourage visual analysis and open it up to all members of a community, not restrict it to those who are data-savvy. A visual display of data allows stakeholders to glean insights with ease without having to be in the weeds.  

Aggregation vs Granularity

One of the features of Tableau is its ability to aggregate (and disaggregate) data. Aggregation occurs when a mathematical function is applied to a measure in a dataset. For instance, our office regularly calculates headcount, or the count of individual students. Other times, we take the average overall GPA. Granularity complements aggregation. For example, if we break average overall GPA down by the students’ academic levels (two groups: undergraduate vs graduate students), then we will increase the specificity, or granularity.

In Tableau, we can add dimensions like academic level to aggregations, increasing or decreasing granularity. Geographic maps tend to illustrate this concept well. They also show another feature of Tableau, which is creating hierarchies in your data. See the example showing student headcount by home address:

Gif that shows student headcount by address, starting with country then US State, and city (focusing on Massachusetts).

These charts show where students are coming from by country, US state, and city (focused on Massachusetts). The darker the color, the higher the headcount. These charts are based on the same dataset, but visually represent the data at different levels of granularity. The world view is the least granular whereas the city view is the most granular.

When we take advantage of Tableau’s natural ability to aggregate data and recognize hierarchies in our data structure, we can easily compare datasets at different levels of granularity. We can quickly zoom in and zoom out depending on what questions are being asked. 
 

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