Data Science

Overview of Data Visualization

The Purpose

The point of a visualization is to communicate its data in a quick and meaningful way while remaining 100 percent accurate. A visualization should serve a clear purpose and not overwhelm the users with unnecessary details. If possible, a visualization should be designed to encourage the users to compare its various elements so as to give insight into the meaning behind the data.

So, to design a data visualization to get its message through, you need to first understand the data itself. Then use well-known design patterns and use the type of visualization that reveals the data in the best way.

Understand the Data

To design an effective visualization with a clear purpose, you should thoroughly understand your data. The following points will help you find the information you want to convey with your data:

  • What kind of data is it? Nominal, ordinal, interval, or ratio data?
  • How different parts of the data relate to each other?
  • Can you organize the data in a way that makes it easy for you to create your visualizations?
  • What do you want to communicate with your data?

It's easy to start thinking about how the visualization should look, but when you have answered these questions it will be easier to decide what kind of visualization you should use and how it will look and communicate its data.

Use Well-known Design Patterns

When you have understood the data, how it is organized, and how its parts relate to each other, you should consider using well-known design patterns to communicate your data. For example, if you want to show how a measure behaves over time, you should use a line chart because its strength is that it tells your users a lot without their having to look at the specific details.

Design Individual Elements to Reveal the Data

Apart from the design pattern you choose, an effective visualization is also about how you design and make individual data elements stand out and reveal the data. In other words, the design of the individual elements of a visualization should reveal the data to your users in a quick and intuitive way. An easy way of achieving this is to use a different color on one of the dots in a group of many dots. The different color makes it much easier for you to find the dot and reduces your load of information. Other examples of intuitive design are:

  • Position
  • Orientation
  • Size
  • Shape
  • Color hue, brightness and saturation

Avoid the Pitfalls of Data Visualization

To experience the benefits of data visualizations you must avoid the pitfalls. Here are some common pitfalls:

Color abuse

Do not overdo colors. Be aware that the wrong color in the wrong place might cause confusion rather than clarity.

Misuse of pie charts

Avoid having pie charts side by side to compare. Try not to squeeze too much information into them.

Visual clutter

Too much information defeats the purpose of clarity. Use a maximum of nine KPIs and remove all visual clutter.

Poor design

A beautiful visualization is not necessarily the most effective. Use design best practices at all times.

Bad data

Spot and correct issues with your data before you present it. Do not let your visualization take the blame for bad information.

Commonly Used Visualizations

In this course, we will study and understand the most commonly used data visualizations.

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