As per Caroline Lee, using data visualization is best to simplify the data and make it understandable.
Data visualization is a potent tool to help quickly distill information, make sense of complex data and help discover trends in your data. It can even be used to improve your understanding of your business. In this blog post, I will share some ideas and practices for using data visualizations in your web applications. I will also discuss why you should use data visualization in the first place.
A new era is dawning in the world of data visualization. Organizations and individuals looking to improve their productivity are turning to data visualization to enhance their decision-making and bottom line. From simple charts to complex graphs, this blog post will take you through some of the most popular practices in data visualization.
Data visualization has quickly become the standard for disseminating information on the internet. From corporate intelligence to journalism, it is used in various industries to help us understand and communicate data results.
Excellent data visualization should take advantage of the strengths of the human visual system to present data in a digestible and understandable manner. It should consider what we know about visual processing to improve and simplify the viewers’ experience with the data.
These top seven data visualization best practices will assist you in creating visually appealing and thorough charts, graphs, infographics, and more.
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Design with a Specific Audience in Mind
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The first step in efficient data visualization and communication is to determine to who you’re showing the data, i.e., the target audience. It could help you tailor your methods and approaches to a specific audience.
Marketers, CEOs, social media managers, entrepreneurs, educators, students, and non-designers are all possible target audiences.
You can create a visual to help identify and represent your target audience, ensuring everyone is on the same page.
For the target audience to be able to assimilate material rapidly, you must visualize it in a clear and intelligible manner.
As mixing and matching representations complicate things, problems develop when the image’s interpretation differs considerably from the conveyor’s goal.
It’s vital to determine the target audience and explain the visualization’s central concept early in the design process. This purpose should be expressed in the graphic design of the visual.
Although those familiar with the process may recognize the statistics in your visualization, others who are inexperienced with the content may struggle to distinguish the facts from the data if the data is not visualized.
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Choose the appropriate graphic for your needs.
Consider the following criteria for common chart types:
- Tables are made up of rows (each row carrying one record) and columns (each column containing several records) (each column is a field). Tables can display a lot of data in a structured way, but for consumers looking for high-level trends, they can be overwhelming.
- Line charts show the relationship between two or more variables over time by recording changes or trends.
- Region charts are similar to line charts, except they have a shaded area below the line. There are minor distinctions between the two, which are best illustrated in this article.
- Scatterplots show the values of two variables on two axes, with the pattern of the resulting points revealing any probable relationship.
3. Make Your Chart More Accessible:
To improve the readability of your represented data overall, ensure that your chart’s design features, such as axis or grid lines, are uniform and precise.
Another essential component is the text you select. While using a lot of words can be distracting, relying just on graphics is insufficient.
Make sure that essential information is highlighted when employing text. Adding language where needed makes a substantial difference, even if our brains are hard-wired to comprehend patterns and symbols over words.
Another thing to keep in mind is that your visualizations should be as clutter-free as possible.
Because data visualization is all about effective communication, it’s crucial that your chart isn’t overloaded with extraneous data that takes away from the most essential features.
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Provide Context: Providing context fosters trust, which in turn leads to action.
The AI era is here, bringing with it the real possibility of a significant transformation in how people work in the future. We could expect considerable increases in staff productivity due to machine learning-powered ideas, which can identify churning clients, power personalized marketing campaigns, and tell you how to optimize your things.
In many cases, tasks will be automated, but experienced users will need to review predictions and recommendations in others. Before making a final decision, folks must first understand the context of the forecast or advice and then believe it. Even in non-machine learning use cases like sales performance, this stays true.
The audience must be able to perceive how the performance compares to something concrete, such as a goal or a previous period’s standard, to evoke a response. In your data visualization, instead of using dynamic thresholds, you should use measures to help your audience grasp the facts they’re seeing. The more context they have, whether or not they use machine learning, the easier it is for them to determine where the action is required.
An excellent data visualization method for showcasing performance is to utilize color, arrows, text, and other visual hints to help readers understand how to evaluate information at a glance.
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Ensure that your data is free of errors.
Before converting your raw data into a graphical representation, ensure the dataset you’re using has been cleaned properly.
Data cleaning is the process of removing any anomalies or inconsistencies from your dataset. You must go through this process before using the data for another purpose, as the presence of these faults can skew the outcomes of your data interpretation.
If you wish to publish a figure in a journal, double-check your calculations and ensure you have all the relevant information. The purpose of this scenario is to clarify the idea, so student audiences will need to think about it carefully.
In that case, you’ll need to go over the subject again to ensure you understand it completely.
A general audience can be challenging to please because you must build a simple, perhaps tentative, graph highlighting your investigation’s essential characteristics.
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Highlight the Crucial Points
By looking at your data visualization chart, the audience must follow the story you’re trying to communicate. This is why it’s critical to use visual cues like reference lines or highlighted trends to grab the reader’s attention.
Humans can process a more incredible amount of information visually, and symbols that give vital information in a single glance capture our attention.
We look for patterns because it’s challenging to interpret what the image expresses if they are chaotic or incoherent.
To draw on these human characteristics, make sure the order or style you deliver the facts makes sense to your audience. You can use data that is numerical, alphabetical, or sequential.
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Use color to your advantage
Colors are a good data visualization approach since they may effectively represent crucial information about your data.
For example, categorical data is best represented using a separate color for each category, whereas sequential data can be organized using different shades of the same hue.
Consider the following points when utilizing color in your data visualization process: If there is a better technique for encoding the most significant values than gradient colors, consider it.
Consider using another map or grouping categories together if a map has more than seven colors.
Final words
Processing, analyzing, and correctly visualizing large datasets has become essential to determine the course of all professional tasks and operations.
Data visualizations in various industries, such as marketing, sales, and business development, inform, describe, and persuade.
It can make all the difference in the world if you use the appropriate data visualization best practices. Adhering to the above guidelines ensures that your data visualizations are always transparent, convincing, and captivating. Hope you are inspired by the information shared on data visualization and put them to good use. In case you need to use free vector icons that you can customize with animations. In the near future, we’ll be back with another intriguing topic. Have fun designing until then!
