Nexa Lab Blog – Creating data visualizations can be difficult. This is due to the fact that when presenting data visually, there are numerous considerations. You might be wondering what type of data we need to present. What chart types should we use to effectively communicate insights? What colour works best for your charts?
That’s why you need data visualization principles.
Data visualization principles are key concepts that enable individuals to effectively communicate insights using visual representations of data. Understanding these principles allows individuals to improve their data storytelling skills and create visuals that are clear, engaging, and easy to interpret for their audience.
Today, we’ll look at several rules and principles that can help you create an effective data visualization. Continue reading to learn more.
What Are the 3 Rules of Data Visualization?
While there are no set rules for data visualization, many people have attempted to develop their own. Office Timeline does, however, lay out some noteworthy rules for data visualization. They broke down the main objective of data visualization into 3 points of rule.
- Translate big numbers into tangible objects.
- Add a visual graphic to your projects.
- Compare your number to historical, future, or comparative figures.
The first rule emphasises the presentation of large numbers. Large numbers may be difficult for your audience to relate to. While everyone understands that one billion or trillion is a large number, it is too large for your audience to relate to emotionally. So, it’s important to break down large numbers into tangible units that your audience can understand, such as miles, pounds, length, and height.
The second aspect is visuals. Choosing the right graph to present specific data can help the audience understand the concept better. For example, if you’re presenting dates, times, or steps in a process, use a Gantt chart or timeline to visually represent your data. The reason this data visualization technique works so well is that it makes your entire project easier to understand and digest than simply listing your dates in a table or textbox.
The third rule is about data comparison. Comparative figures are another important aspect of data visualization for specific types of information. For example, having quarterly sales of $4.5 million is meaningless unless it is compared to something else. However, if you show a decrease or increase compared to the previous quarter, the data will speak for itself and help the audience understand the big picture of the information.
Before we get into other data visualization principles, you should first understand the fundamentals.
You can read more about it in our previous article, ‘What Is Data Visualization? Definition, Importance, and Examples‘.
6 Data Vizualisation Principles for Data Analysts
Martin J. Eppler, a professor of communication and management at Cambridge University, has developed another interesting data visualization principle. In his post, he abbreviated the six data visualization principles as DESIGN, to help us remember them more easily.
In his principle, DESIGN means:
- D eclutter
- E mphasize
- S torify
- I nvolve
- G ive meaning
- N o distortions
Let us break down each of those principles and learn how they can help you create a successful data visualization.
1. Decluttering
Removing anything that takes the eye away from your data is the first step in decluttering a chart. Therefore, eliminate any borders, prominent grid lines, extraneous details (like decimals), 3D effects, (overuse of) colours, shades, and other decorative elements (like ostentatious animation schemes).
2. Emphasise
To emphasise something is to first select a graphic format that best communicates your main idea or the purpose of the chart. Secondly, visually highlight the most significant component in the chart by, for instance, underlining or changing the colour of the element.
To help you choose the right chart format, you should ask yourself
- Do you want to enable comparisons? Then choose vertical bars.
- Do you want to enable a ranking from smallest to largest? then choose horizontal bars.
- Do you want to show a trend over time? Then use a line chart.
- A last option would be to emphasise deviations from a goal or reference (such as a budget plan). In this case, choose upward and downward vertical bars.
3. Storify
Presenting a chart (or a series of them) in a way that allows you to narrate an engaging story about the numbers you are displaying is known as “storifying data.” To do that, you can splits your data into a trilogy.:
- Setting the scene (an overview chart that clarifies the situation)
- Showing the complications (one or several charts to show more details)
- Providing a resolution, i.e., charts that show opportunities for action.
Adding emotion and a unique visual style to your chart are additional ways to storify it, if applicable. Do “selling before telling” to establish a connection with the audience by making the information relevant to them.
4. Involve
When you create and present a chart, you must consider your intended audiences in order to involve them in the context of the data. This can be accomplished by offering your users easy ways to offer feedback on the chart and by allowing them to click on the chart to dig deeper into their exploration.
Through the ability to choose areas of interest, zoom in for more information, examine various data aspects, remove elements from the display, alter the display, and link the data to new sources, you can invlove users to your data using an interactive chart.
5. Give meaning
Making data meaningful for your audiences is the main goal of data visualization. You can support this sense-making process in several ways. Some of the most important are:
- One method for making data more meaningful is to link it directly to potential actions or responses (think bar chart on the left, recommended actions on the right).
- Another option is to give the chart an action title that expresses its meaning.
- Including self-explanatory labels and axes descriptions in a chart makes it more meaningful, even for hurried viewers.
6. No Distortions
Generally speaking, this last principle advises against using graphic formats that make data hard to read or easily misinterpreted.
Some examples of sub-optimal chart formats are pie, doughnut, and arch charts, which are difficult to compare and perceptually inefficient.
Other sub-optimal chart formats include stacked bar and area charts, which have moving baselines, or charts that combine units and have two distinct y axes in one image.
Additionally, you want to steer clear of line charts with a lot of crossing lines because they are particularly difficult to read because of all the overlaps and intersections.
Now you’ve got it. We hope these rules and principles help you create a data visualization that effectively communicates your insight. If you’re still not convinced why you should create a data visualization, check out our article, ‘5 Advantages of Data Vizualisation: With Best Practices for Implementation‘.
Conclusion
Understanding the fundamentals of data visualization is crucial for effectively sharing insights in the data-driven world of today. Data analysts can produce visuals that engage stakeholders and promote well-informed decision-making by following these principles.
Ready to take your data visualization to the next level?
Nexa Lab Data Visualization and ETL services provide you with cutting-edge data visualization services that will help you make sense of your data and drive strategic decision-making. We offer the best services for you with interactive dashboards, data integration and aggregation, advanced analytics and forecasting, and automated ETL workflows.
Nexa Lab is a web and application developer that specialises in MSPs (Managed Service Providers) and IT departments. We were born and raised in Australia and have over 30 years of experience in the MSP and IT industries.
