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Data Visualization Best Practices: Telling Stories with Data

According to researchers who conducted the Rochester review, the human brain uses 50% of its capacity to comprehend visual information. More than half, i.e., 65% of people, are visual learners, according to another study, meaning that most people need to see knowledge to retain it. Since data visualization holds such importance, one needs to learn data visualization best practices in order to attain ultimate success in telling stories through data. 

What Do Experts Say?

"If you can't explain it simply, you don't understand it well enough." 

- Albert Einstein 

(Famous Scientist & Philosopher)

Here, Albert Einstein highlights how crucial precise and succinct explanations are to comprehending anything. When it comes to data visualization, making difficult information visually understandable assists in others' comprehension of its meaning. Visualizations that are unduly complex or challenging could be a sign that the presenters themselves do not well understand the story of the data.

One must adopt best practices for data visualization that can impart simplicity. Data visualization design best practices must essentially ensure that the audience can easily understand and interpret the information being presented. 

Let us delve into knowing the best practices in data visualization along with their implementation for a successful career in data.

Here is what we will cover in the article:

The Use of Data Visualization 

Data Visualization is information that has been schematically abstracted and includes variables or attributes for each data element. Visual communication of data or quantitative content can be achieved through various methods like line graphs, bar charts, pie charts, scatter plots, or maps. And Willard Cope Brinton rightly said, "A good graph does not look complex, but one that is complex and yet understandable." 

Since the human brain needs abstractions or analogies to understand numbers greater than five, big data is useless if it cannot be efficiently processed and interpreted by the brain. Data visualization designers can play a vital role in creating those abstractions by embracing best practices data visualization has to offer.

Important Features of Good Data Visualizations

Any good data visualization must possess the most relevant features:

  • Precise: The data and its patterns must be precisely depicted in the visualization.
  • Transparent: Your visualization needs to be understandable and clear.
  • Actionable: After interpreting your visualization, the viewer should know what to do.
  • Concise: A message should be easy to understand and should not take a long time to get through.
Important Features of Good Data Visualizations

Discussing the Best Practices of Data Visualization

Before beginning the process, anyone who has the desire to create a visualization must ask oneself the following questions:

  • Who is my target audience here?
  • What queries do they have?
  • What solutions am I discovering for them?
  • And what do I want to say?
  • What other queries or discussions might arise from my visualization?

After you know the answer to these questions, the journey toward successful data visualization becomes a piece of cake, especially when you are aware of the data visualization best practices needed for your specific task.

Best Practices of Data Visualization

Know Your Purpose Clearcut

One must attain clarity in terms of defining the purpose of data visualization. Data visualization best practices include addressing strategic questions, delivering real value, and decoding definite concerns. Spend some time up front defining the goals and objectives of your visualization project. This clarity streamlines your efforts to produce a more impactful result by preventing the development of useless graphics. By identifying a primary motivation and classifying the data into ad hoc, strategic planning, and decision support, you may produce an understandable and powerful data visualization.

Know Your Audience

If you want your visualization to be effective, it has to reverberate with the targeted audience mindset. Make sure the data is easy to read, and adjust your design based on the audience's level of experience. Take into account the audience's knowledge of underlying concepts and, if relevant, their experience in STEM subjects.

Use Correct Visual Features

You need to use the correct type of chart, as it can greatly influence understanding. Choose the structure for your visualization carefully so that it best serves your core goal by answering important questions raised by the data and telling the story. Even merging similar charts can be useful at times; it can encourage more in-depth research that yields useful business insights and solutions that motivate action. Various charts have different functions.

Different Types of Charts for Data Visualization

  • Line Charts: Perfect for contrasting values across time.
  • Bar Charts: Useful for comparing numerical data between different categories. Bar charts are a reliable method for comparing numerical data between different categories, with the y-axis starting at zero unless otherwise indicated.
  • Bullet Charts: Created to replace dashboard gauges, meters, and thermometers by comparing metrics to demonstrate progress toward a goal.
  • Scatter Plots: Good for showing the values of two variables together.
  • Pie Charts: Useful for showing individual components of a whole. These should be used cautiously due to potential deception about size.
  • Box plots and histograms: They can compare categories and display the groupings of your data.
  • Maps: These are an obvious tool for helping with geographical exploration or visualizing questions related to a certain region.

Design Layouts with Predictable Patterns

Humans, as we all know, naturally look for patterns to understand quickly. Data should be arranged logically (either numerically, alphabetically, or sequentially), and layouts should follow language reading conventions. Maintaining a consistent arrangement across several graphs improves readability and reduces confusion for viewers when exploring the data.

Make It Neat and Organized

Most importantly, you need to organize the big dataset into a readable visualization. A well-thought-out visualization ought to blend into its surroundings, making it easy for an audience to process the data. For a clearer depiction, hierarchy-building, color selection, and strategic element sizing are important.

Make It Simple

Note that the secret to a good narrative is simplicity. Aim for precision and simplicity, selecting the most straightforward approach to convey your story. Retain simplicity even with intricate data by keeping a chart's categories small and staying away from needless complexity.

Construct Data Visualization Inclusive

It is good that you evaluate accessibility in your data visualization. Increased readability is a result of high contrast, pattern complimenting hue, and labeling with text or icons. Make sure your font sizes are readable, and select simple typefaces to make your message clear to a wide range of people, including those who are visually impaired.

Avoid Any Data Distortion

A very crucial ‘don’t’ to address here is avoiding visual distortions, as it is helpful in preserving transparency and accuracy. Representations that could mislead viewers, such as 3D pie charts, in some cases, should be avoided. By carefully selecting colors and emphasizing particular data points, you can improve storytelling without sacrificing data accuracy.

Avoid Clutter, Implement Cluster

Do you remember Gestalt principles? Use a few, like proximity and similarity, to organize complex data. To group related components together and cluster data logically, use proximity and color. Make patterns that improve comprehension without being too overwhelming for the audience.

Convey Data Stories Efficiently with Clear Color Cues

The power of color is too important and impactful to use in your data visualization techniques to communicate facts efficiently. To prevent misunderstanding, keep the use of color simple. Use color to highlight and emphasize important information, but do not use too many colors at once. Select colors that make sense and are consistent with what viewers expect. Applying color consistently highlights contrasts and preserves continuity across data values.

Enhance Understanding with Contextual Shapes and Designs

Contextual cues provided by designs and shapes facilitate the rapid absorption of facts. For a more captivating visualization, consider substituting typical bar charts with silhouettes (for example, endangered species or gender-specific figures, etc.) relating to the concept being discussed. An intuitive presentation and gripping narration are enhanced by the use of shapes.

Employ Size Strategically for Visualizing Values

Do you know that size is a mighty tool that is used to stress over a piece of information and deliver context? Use size to represent scaled values in order to distinguish between data points properly. This method is effective for maps where color acts as an extra marker and size communicates values. Make careful size adjustments to highlight important details and improve browsing holistically.

Use Text Purposely for Extra Insight Effect

Designers and presenters must understand that text enhances visuals by providing necessary details and enhancing comprehension. The text should be deliberately arranged, with key information in the upper-left or top-of-the-screen area. To keep the story focused and promote exploration, integrate interaction sparingly, organize filters for clarity, and limit the number of views.

Make Up Good Story

Last but definitely not least, data visualization is about narrating a compelling story. And it is not just limited to mere fact presentation. Place the demands of your audience first, and use donuts or pie charts to illustrate the idea of the whole and its components from time to time. Prioritize the audience's resonance while making decisions about your visualization.

FAQs About Data Visualization Best Practices

Q1. How does data visualization act as a tool for storytelling to management?

A larger audience can better access and comprehend the data that data storytellers present by leveraging data visualization, charts, and other tools. Data storytelling is crucial since it makes data more meaningful to the audience.

Q2. Why is storytelling so important in data visualization?

Through the organization of unprocessed data into data visualizations, data storytelling enables the development of a clear and concise storyline that propels business choices.

Q3. What are the four main parts of storytelling in data visualization?

Data analysts and business executives should incorporate four essential components into their data stories to motivate their teams to take action. Character, setting, conflict, and resolution are the four elements of every effective narrative arc, and they are also the basics of data storytelling.

Q4. Which tool is best for data visualization?

Tableau, Grafana, Google Charts, Chartist, Datawrapper, FusionCharts, Infogram, and ChartBlocks are a few of the top data visualization tools. These tools are simple to use, support a wide range of visual styles, and have a high data-handling capacity.

Q5. How to do data visualization in Excel?

Sort the relevant data in Excel, click "Insert," select a chart type, and then insert the chart. Use the "Chart Tools" tabs to alter appearance. Modify the worksheet's data to trigger automated chart updates. Try different chart styles and types to see what works best for a given set of data.

Q6. What are the disadvantages of data visualization?

Inadequately crafted visual aids may result in incorrect data interpretation or communication. Drawing the wrong opinions might be caused by misleading or unclear images. Data can occasionally be inadvertently misrepresented—or even intentionally—or your message may become diluted. 

Q7. What are the four pillars of data visualization?

Data visualization is based on the article "4 Pillars of Data Visualization" by Mahbubul Alam. The four different pillars are as follows:

  1. Distribution: relating to one single variable
  2. Relationship: between two variables
  3. Composition: single or multiple variables
  4. Comparison: between different categories/individuals
Last updated on: 
January 12, 2024
Author

Bareeha Dehradunwala

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