Best Practices for Data Visualization (2023)

The process of converting data into understandable pictures is known as data visualization. The visual depiction of numerical data using different graphs, charts, and maps is known as data visualization. These graphics could be animations, graphs, charts, maps, plots, and more. It’s crucial to adhere to specific best practices for data visualization because it’s critical to understand massive volumes of data.

Data visualizations are crucial in assisting individuals in comprehending complex data and spotting patterns and trends over time. Data visualization is a crucial ability for data professionals and frequently goes hand in hand with storytelling to effectively communicate observations and guide decisions.


• Using data visualizations to illustrate trends, patterns, and correlations in data is helpful.

• Key decision-makers can use data visualization as a powerful tool to make well-informed decisions supported by data.

• Using data visualizations makes it simpler to aggregate numerous datasets from many sources and make sense of enormous datasets.

• It’s a powerful technique for using storytelling to convey concepts supported by facts.

The target audience’s attention must be maintained while yet being simple to comprehend and use.

• Using data visualizations makes it simpler to keep an eye on key performance indicators and monitor crucial metrics (KPIs).

Best Practices

1. Establish a specific goal.

Data visualization should deliver genuine value, address important strategic challenges, and aid in problem-solving. For example, it can be used to monitor consumer behavior, track performance, and assess the efficiency of procedures. The end output will be more valuable, and less time will be wasted if the goals and priorities of a data visualization project are clearly defined at the beginning.

2. Identify the target market

The first stage of inefficient data visualization and communication is determining the target audience or the group of people for whom one is visualizing the data. This helps to adjust approaches and methods to suit a certain audience. Identifying the intended audience and communicating the visualization’s main idea as early in the design phase as feasible is critical. This purpose should influence the graphic design of the image.

3. Verify the Data Is Clean

One must ensure that the dataset has been adequately cleaned before turning the raw data into a graphical representation. The process of eliminating any irregularities or inconsistencies from the dataset is known as data cleaning.

Before using the data for another reason, one must go through this process because these errors might also skew the outcomes of the data interpretation.

4. Set a goal

Understanding the type of data one is dealing with and having a concept of the insights hopes to gain from it will help one design data visualizations. The information one wishes to convey, its significance, and the audience one presents to should all be kept in mind.

5. Select the Appropriate Visualization Tool

After establishing the visualization’s objectives, think about the best tools to use when presenting the data. Depending on the use case, there are code libraries and no-code/low-code platforms, and each has its advantages.

6. Use layouts that follow a recognizable pattern.

Human eyes quickly pick up cues that aid in comprehending crucial information. We must understandably display data for viewers to learn about human thought. Keep viewers from becoming confused because it is not one of its best practices, and users can easily go from one point to another.

7. When illustrating data tales, use appealing colors.

A lot of communication aboutThemkjj visuals is helpful. One of the finest practices for it is to keep things simple. Without using words, graphs can be effectively represented using color. Using color to highlight important information makes the dashboard easier to grasp more regularly. Correct color clubbing is crucial. Try to utilize natural colors so that the spectator can comprehend quickly. For instance, to display a profit for a given month on a graph, use several shades of the same color, with the brightest for that month showing the largest profit.

8. Make use of simple and appealing language Dashboards

Try adding three or four charts or graphs to make the dashboard easier to grasp. As we all know, dashboards typically feature a lot of graphs. Try to use different colors for different figures to help viewers understand the data and make informed judgments. The dashboard is the key tool for this. The viewer’s attention can be captured if the dashboard is made to be both clear and appealing.

9. Make Labels and Titles Brief

The visualizations should have titles and labels and be simple to understand. These labels should set the scene and explain what the graph is attempting to show to the viewer.

Utilizing simple-to-read typefaces, making sure the fonts are large enough, paying attention to where labels are placed, and using colors that are also easy to read are additional crucial best practices for titles and labels.

10. Steer clear of clutter and extraneous visual elements

Effective data visualization is clear, interesting, and uncluttered. When too many things happen at once in visualization, the audience may need to focus on the lessons one wants them to learn.

Use straightforward designs and avoid superfluous features to make the representation easier to comprehend. Unnecessary components can include too many labels, distracting patterns and graphics in the background, extraneous data points, and irrelevant gridlines. Ensure the headers and labels are simple to read and understand and check that charts and other components are spaced appropriately.

Conclusion – 

Effective data visualization should use graphics to communicate a data set clearly and understandably. The most effective visualizations make it simple to understand data quickly. They simplify complex material so the target audience can easily understand it and use it as the basis for their judgments. It is now essential to process, analyze, and efficiently visualize massive datasets to influence company decisions and establish the direction of all professional activities and operations. The right data visualization best practices can make all the difference in the world. By adhering to the tips described above, one can ensure that the data visualizations always stay understandable, convincing, and compelling.

Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone's life easy.

↗ Step by Step Tutorial on 'How to Build LLM Apps that can See Hear Speak'