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You’re a data visualization expert. How do you avoid common misconceptions?

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Data visualization is a powerful skill that can help you communicate complex and insightful information to your audience. But it also comes with some challenges and pitfalls that can lead to misunderstandings and errors. As a data visualization expert, you need to be aware of these common misconceptions and how to avoid them. In this article, we will cover six tips that will help you create clear, accurate, and engaging data visualizations.

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1 Know your data

Before you start designing your data visualization, you need to understand your data and its context. What is the source, quality, and reliability of your data? What is the main message or story you want to convey? What is the best way to represent your data type, scale, and distribution? Knowing your data will help you choose the right format, style, and level of detail for your data visualization.

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Mohamed Meqlad

Data engineer || 3 X Azure certified || Kaggle master
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To avoid common misconceptions in data visualization, ensure clarity by choosing appropriate visualization types. Always label axes clearly and provide context for data interpretation. Avoid misleading scales or omitting relevant data. Stay mindful of biases and ensure representations accurately reflect data. Finally, seek feedback to improve understanding and effectiveness.

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2 Know your audience

Another important factor to consider is your audience. Who are they, what do they know, and what do they need? How will they access, interpret, and use your data visualization? Depending on your audience, you may need to adjust your tone, language, and complexity of your data visualization. You may also need to provide some background information, definitions, or explanations to help them understand your data.

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Stacy N.

Portfolio Management | Broad-based knowledge to deliver profitable investments | Associate in the Chartered Institute for Securities & Investments (ACSI)
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As a data visualization expert, it's important to be aware of common misconceptions and avoid falling into them. One of the most important steps is to know your audience and tailor your visualizations to their needs and level of expertise. You should also strive to use accurate and relevant data, present it in a clear and concise manner, and avoid distorting or manipulating the information to fit a certain narrative. Additionally, it's important to be aware of common biases and limitations in data and to acknowledge them in your visualizations. By being diligent and mindful, you can create effective and informative data visualizations that serve your audience well.

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3 Choose the right chart

One of the most common misconceptions in data visualization is that any chart can fit any data. This is not true. Different charts have different purposes, strengths, and limitations. For example, a pie chart is good for showing proportions, but not for comparing values or trends. A line chart is good for showing changes over time, but not for showing categories or distributions. A bar chart is good for showing comparisons, but not for showing relationships or correlations. You need to choose the right chart that matches your data and your message.

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Anand K. Chandarana

Director of People Analytics & Insights at Cencora | MBA, SPHR?
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Don't rely on the hundreds of "chart chooser" diagrams, cheat sheets, etc. that you can find online. Almost all of them will have more than a few "one-size-fits-all" overgeneralizations, inaccuracies, and just plain bad data visualization options in them. As a rule of thumb, the more chart choices they present, the more bad chart choices they've included.

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4 Avoid distortion

Another common misconception in data visualization is that you can manipulate your data or your chart to make it look more impressive or convincing. This is not only unethical, but also counterproductive. Distorting your data or your chart can create confusion, mistrust, and backlash from your audience. Some examples of distortion include using misleading scales, axes, or labels; hiding or omitting data; using inappropriate colors, shapes, or effects; or adding unnecessary elements or decorations. You need to avoid distortion and present your data as accurately and honestly as possible.

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5 Use design principles

Data visualization is not only about data, but also about design. Design principles can help you make your data visualization more appealing, readable, and understandable. Some of the basic design principles include contrast, alignment, hierarchy, balance, and white space. Contrast helps you highlight the most important or relevant data points. Alignment helps you create a consistent and organized layout. Hierarchy helps you show the relationship and order of your data elements. Balance helps you create a harmonious and symmetrical composition. White space helps you avoid clutter and create breathing room for your data.

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6 Test and refine

The final tip to avoid common misconceptions in data visualization is to test and refine your data visualization. Testing means asking for feedback from your audience or other experts, or using tools or methods to evaluate the effectiveness and usability of your data visualization. Refining means making adjustments or improvements based on the feedback or evaluation results. Testing and refining can help you identify and fix any errors, gaps, or flaws in your data visualization, and ensure that it meets your goals and expectations.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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You’re a data visualization expert. How do you avoid common misconceptions?

1

2

3

4

5

6

7

1 Know your data

2 Know your audience

3 Choose the right chart

4 Avoid distortion

5 Use design principles

6 Test and refine

7 Here’s what else to consider

Data Visualization

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You’re a data visualization expert. How do you avoid common misconceptions?

1

2

3

4

5

6

7

1 Know your data

2 Know your audience

3 Choose the right chart

4 Avoid distortion

5 Use design principles

6 Test and refine

7 Here’s what else to consider

Data Visualization

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