Creating high-impact visualisations can seem like a daunting task. Straying from what analytical software provides out of the box is to wander into terra incognita for some.
Do I use the stock standard blue and orange line graph supplied by Excel, or do I mix in some personality to these graphs? I want some more colour, some more interaction, some more pizazz! But how do I do this?
You're in luck. There is a checklist available to us courtesy of the brilliant minds of Stephanie Evergreen and Ann Emery.
The checklist stipulates that text be used sparingly. The driving force behind visualisation is one that moves away from using text in a raw format. Any text outside the following must pack a punch or be removed.
Ensuring that data points and visual elements are presented in an organised manner is critical for successful data visualisation. The correct arrangement helps viewers comprehend the information more quickly, while incorrect placement can result in confusion or misunderstanding. Consequently, the proper interpretation of graph elements is essential for successful visualisation.
Choosing a colour for a design or print can be tricky, as certain colours have specific cultural connotations and meanings. For example, red is often associated with passion, while blue is thought to represent loyalty. To ensure you choose the right colour, use tools such as Color Brewer that provide elaborate colour schemes suitable for colour printing and colourblind people.
Lines, such as gridlines, borders, tick marks, and axes, can often make a graph more cluttered and difficult to interpret. Whenever possible, any unnecessary lines should be removed to improve the readability of the data.
When visualising data, it is essential to ensure that only the most critical information is being presented via graphs. More non-essential information can lessen the impact of visualisation; therefore, it is crucial to focus on what truly needs attention. This will ensure that charts can help catch the viewer’s eye and convey information effectively.
How well are your visualisations scoring on DataViz checklist?