The Royal Statistical Society (RSS) provides useful resources for any expert planning to use data visualisation in reports, always keeping in mind the intended audience and that how you display data can strongly influence the way data is perceived so that graphs need to be designed to be objective and not skew the reader's perception.
The process of deciding what type of data visualisation to create begins with a simple question: Why am I doing this? Data visualisations must serve a purpose so all decisions about chart type, design, layout, and more, should flow from a clear understanding of the intended purpose of a graphic.
Christian Hennig, a statistics professor at the University of Bologna, suggests working through the following questions:
1. Is the aim of the graph to find something out (“analysis graph”), or to make a point to others?
2. What do you want to find out?
3. Who is the audience for the graph? (It may be yourself.)
Identifying the target audience at an early stage is crucial, as different groups of people are likely to have different levels of graph literacy depending on their education level, technical expertise, prior exposure to data visualisation formats, and other factors. Design decisions that do not properly account for the needs of the intended audience will fail to achieve their aims.
Checklist
Before any design decisions are made:
Be clear about the intended purpose of a data visualisation.
Understand your target audience, including their needs and familiarity with different visualisation types.
Data
Software tools generally allow users the flexibility to visualise data in whatever format they choose. However, decisions about chart type must be informed either by the kind of data at hand or the data relationship that is of interest. Format choice must then be further guided by audience needs, as previously discussed.
There are a range of online tools to help decide on a visualisation type. The RSS recommends From Data to Viz who present users with a series of decision trees, each leading to different recommended chart formats depending on the type of data selected (numeric, categoric, etc.).
Further reading:
Best practices for Data Visualisation https://royal-statistical-society.github.io/datavisguide/
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