Visualizing Data Effectively

Presenting data in the right form allows for effective interpretation. There are many types of charts that can be used to visualize data. However, certain types of charts are better suited than others to depict particular types of data or answer certain types of questions. This chapter examines some basic chart types, which are available in Looker Studio, and their appropriate use. We will also look into some data visualization pitfalls that are commonly observed and understand how to avoid or mitigate them.

In this chapter, we are going to cover the following main topics:

• Choosing the right visuals
• Avoiding common pitfalls

Choosing the right visuals

Using the right chart types to represent data is perhaps the most important design decision you need to make as a dashboard developer. In this section, we will review the most common types of charts and discuss how to use them appropriately.

Figure 3.1 – Common chart types

Note

There are additional visualization types available to the ones discussed in this chapter that are used less widely. The objective here is to introduce only the most basic chart types rather than offering a comprehensive list. You can refer to the resources listed in the Further reading section of the chapter for broader and more in-depth coverage.

Different chart types are suitable for representing different types of data. Broadly, data can be either categorical type or numerical type. Examples of categorical data include countries, gender, education levels, and risk groups. Categorical data can be measured either on a nominal scale or an ordinal...

Avoiding common pitfalls

In this final section of the chapter, we will go through some of the common pitfalls and gotchas to watch out for while designing dashboards and visuals. You will see that these usually involve either inadequate application of the design principles we have discussed in this chapter or a complete lack of adherence to them.

Overloading a dashboard

A dashboard that tries to convey too much is an overloaded dashboard. It is tempting for dashboard developers to respond to users’ relentless demands for additional information by adding more and more visuals and information to an existing dashboard. This tendency will only result in a cluttered dashboard that will be cumbersome to use and understand.

Also, trying to address the needs of different groups of users with the same dashboard is a bad idea. Limit the scope of the dashboard and align it with a single major objective and persona. Provide additional details to the users through separate dashboards...

Summary

Using the right chart type to display information is of paramount importance. In this chapter, we reviewed a selection of common chart types to help you make the right choice for your needs. We discussed some of the common pitfalls and gotchas in visualizing data and how to avoid them.

This marks the end of part one of the book, which focused on the concepts of data storytelling and guiding principles. In part two of the book, we will explore Google’s data visualization tool Looker Studio and its features and capabilities. The next chapter introduces the tool and examines its major components.

Further reading

To learn more about visualizing data effectively, you can refer to the following resources:

• Knaflic, Cole Nussbaumer. Storytelling with data. John Wiley & Sons. 2015.
• Berinato, Scott. Good charts. Harvard Business Review Press. 2016.
• Jones, Ben. Avoiding data pitfalls. Wiley. 2019.
• Chart Types. FlowingData. https://flowingdata.com/chart-types/.
• The Data Visualization Catalogue. https://datavizcatalogue.com/.