Choosing an Appropriate Data Source Type

Before any user or developer can start building a visualization, an appropriate data source must be defined. Without data, there will be no visuals to be built and no stories to be told. Multiple factors should inform decision-making when it comes to choosing a data source. In summary, these include the following.

Content and Quality

The following points are key for any data preparation for a developer to be able to analyze data properly. It is important for you to familiarize yourselves with these practices as you will be questioned about them in the exam.

Level of detail: Dimensions and Measures

Tableau should be approached not merely as a tool for data visualization. Charts should be created and used thoughtfully as a means of answering business questions. Therefore, data should be selected with a goal in mind: does it contain the fields (columns) and records (rows) required to answer the questions at hand?

When it comes to fields, data should contain the appropriate dimensions (to divide the view) and measures (for assessable metrics). It is impossible to review the relative performance of each salesperson, for example, without their name or other unique identifiers alongside a Profit field. And if those fields are incomplete – lacking all salespeople, or profits for certain months of the year – then accurate conclusions cannot be drawn.

Data Quality

The previous point touched on completeness as an important facet. This can be expanded further: any data source used should have an appropriate level of completeness, accuracy, and consistency for the resulting insights to be valuable. You need to make sure that the data used is complete, all field names are named appropriately, and the spellings are kept consistent. Please see Chapter 2, Transforming Data, for further details.