How to present ethical and unbiased visualisations in your data communication.
The summary of a story or event is a comprehensive presentation of the story's main points. Summaries, whether pictorial or text-based, are direct and make it easier to grasp the main gist without the attending frills. After a thorough analysis of the data, it is presented to stakeholders in a summarised pattern that they would grasp without difficulty. This is where data visualisation comes in.
Data visualisation is the presentation of information in a graphical fashion. Data visualisation involves the use of charts, graphs, maps, etc, to aid analysis and make decisions.
Data can be presented in its raw and unclean form but that would just be stressful and inconvenient. When there is data visualisation, people can quickly understand data no matter how complex and technical it is and no matter how experienced they are. Storytelling with visual data also applies to every industry and field.
Why you should follow the right ethics when presenting your data.
According to Alberto Cairo, a designer and journalist, it is morally right to create graphics that contain accurate and relevant information. Presenting honest and relevant information tends to impart knowledge and increase understanding.
When visualisations are confusing and hard to understand, it tends to give misleading information. This is unethical no matter the intent. Before you create a visual, think about your viewers and the consequences the visuals might have on them.
How to save your data visualisation from being misleading and unethical.
- Start your chart with zero.
This might look minor but happens so often and gives a distorted view of data leading to confusion and misunderstanding. It gives an undeserved bump to charts and it is unethical.
- Don't include too much information in a visual.
Remember that too much information in one visual can overwhelm your audience. Instead of using only one visual, try making a dashboard containing different charts/graphs to further drive your point. Your audience would appreciate and grasp this better. Quality is always better than quantity.
- Always use reliable data from reliable sources.
You can only make reliable visualisations from reliable information gotten from reliable sources. Always take out time to ensure that your data comes from a primary source and if possible, verify that it is legitimate. If it is biased or influenced in any way, please apply caution when using such data. Ask relevant questions to the experts in the field from which the data was collected to be doubly sure about its reliability. Any shortfalls should be made clear in your presentation.
If your chart has any outliers, be sure to include them as long as they are relevant information. If you withhold any information, you’re unconsciously introducing a bias and not showing integrity.
- Your color choices are very important.
Color choice is an important consideration in data visualisation. Using too many colors or just throwing in colors that don't match together will ruin your visuals. When it comes to colors, it's important to be minimalistic and study color spectrums to ensure that the chosen colors go well together.
Always provide context for your visualisations.
To aid easy understanding, always add helpful comments, titles or notes to your charts. These will help your audience and minimise misinterpretation.
Avoid any kind of bias.
When reporting and presenting data, it is important to not be selective about the truth. The information in the data should be more than enough to drive the storytelling. Avoid any kind of bias in order not to influence your stories unduly.
- Use appropriate visualisations
The purpose of data visualisation is to communicate information needed by stakeholders without frills and bells. Unnecessary add-ons that do not contribute to this should be avoided to avoid clutter and misinformation.
- Don’t try to fix it if it is not broken.
When making visualisations, it is important to follow already laid down guidelines. Especially with colors, some convey positive information and some do not. Trying to do something different will only confuse and mislead your audience.
Conclusion.
It is easy for data analysts to unknowingly behave unethically during the presentation of data to stakeholders. However, as data personnel, we have a responsibility to present our visualisations in a non-biased way and not influence the message to the audience in any way. Data visualisation when done correctly can make all the difference.