Learn some of the most reliable ways to create accurate data visualizations that convey your message and resonate with your audience.

Garbage in, garbage out. Data preprocessing is the most critical step of any data visualization effort. Before struggling with chart types or font choices, spend most time on checking and preparing data first. Because no matter how fancy the visualization, if the underlying data is flawed, it undermines the entire objective and can misinform the audience.

What are the most reliable ways to create accurate data visualizations?

Data visualization is the art and science of presenting data in a clear, engaging, and meaningful way. It can help you communicate insights, tell stories, and persuade audiences. But how do you create accurate data visualizations that avoid misleading or confusing your viewers? Here are some of the most reliable ways to ensure your data visualizations are accurate and trustworthy.

1 Choose the right chart type

The first step to create accurate data visualizations is to choose the chart type that best suits your data and your message. Different chart types have different strengths and weaknesses, and some are more appropriate for certain types of data than others. For example, bar charts are good for comparing discrete categories, line charts are good for showing trends over time, and pie charts are good for showing proportions of a whole. Choosing the wrong chart type can distort your data or obscure your point.

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Tianyu Xu

I help people acquire future-proof generative AI skills → Author | Speaker | AI Educator
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Garbage in, garbage out. Data preprocessing is the most critical step of any data visualization effort. Before struggling with chart types or font choices, spend most time on checking and preparing data first. Because no matter how fancy the visualization, if the underlying data is flawed, it undermines the entire objective and can misinform the audience.

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2 Follow data visualization best practices

Accurately creating data visualizations requires following some basic data visualization best practices that can improve the clarity and readability of your charts. Ensuring consistent and appropriate scales, axes, and labels is key, as is avoiding clutter and any unnecessary elements that could distract from the data. Additionally, utilizing colors, shapes, and symbols that are easy to distinguish and interpret is important. Titles, captions, and annotations should explain your data and the message you are trying to convey. Lastly, citing your data sources and providing context and definitions is essential.

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3 Check your data quality and accuracy

The third step to create accurate data visualizations is to check your data quality and accuracy before and after you visualize it. Poor data quality and accuracy can have a detrimental effect on your credibility and reputation, so it's important to ensure that your data is complete, consistent, valid, and reliable. To do this, you should clean and preprocess your data to remove errors, outliers, duplicates, and missing values. Additionally, you should validate your data to make sure it matches the original source and intended purpose. Finally, you should test and review your data visualizations to spot any errors, anomalies, or inconsistencies.

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4 Seek feedback and iterate

In the fourth step to create accurate data visualizations, you should seek feedback and iterate on your charts. Doing so can help identify any flaws, gaps, or ambiguities you may have overlooked, as well as gauge how well your data visualizations convey your message and resonate with your audience. To do this, share your data visualizations with people who have different perspectives, backgrounds, and expertise. Ask for constructive and honest feedback on the data, design, and message. Incorporate the feedback that makes sense and enhances your data visualizations. Repeat the process until you are satisfied with the results.

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5 Be honest and ethical

The fifth step to create accurate data visualizations is to be honest and ethical with your data and your charts. This is essential for respecting your data, your audience, and yourself. To be honest and ethical, you must avoid manipulating or misrepresenting the data to fit an agenda or bias, as well as using deceptive or misleading techniques that distort or exaggerate the data. Additionally, it’s important to acknowledge the limitations and uncertainties of your data and your charts, as well as any conflicts of interest or influences that could affect them.

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Tianyu Xu

I help people acquire future-proof generative AI skills → Author | Speaker | AI Educator
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Is it too much to recommend a book here? The classic “How to Lie with Statistics” by Darrell Huff, published in 1954, is a must read for anyone in a data-related field. While its title might suggest otherwise, the book doesn’t instruct readers on how to lie. Instead, it equips people with the skills to catch misleading data and educates on using statistics ethically.

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6 Keep learning and improving

The sixth and final step to create accurate data visualizations is to keep learning and improving your data visualization skills and knowledge. Data visualization is a dynamic and ever-evolving field, so it's important to stay up-to-date with the latest trends, tools, and techniques. To do this, you should read books, blogs, articles, and podcasts on the topic. Additionally, you should explore different data visualizations from various sources and domains. Practicing and experimenting with different data sets, chart types, and tools can also help you improve your skills. Finally, it's important to seek feedback on your work and learn from both your mistakes and successes.

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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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Tianyu Xu

I help people acquire future-proof generative AI skills → Author | Speaker | AI Educator
Report contribution
Final thoughts: can AI help? My answer is yes. Large language models will have a significant role to play in creating reliable data visualizations. Despite the annoying hallucinations, LLMs continue to surprise us with every update. Here are three ways you can already utilize a tool like ChatGPT in this context: 1. Data cleaning 2. Data visualization 3. Check and revise By 2024, I expect that the number of errors LLMs make will be significantly fewer than those made by humans, just like other mature AI technologies such as facial recognition and OCR.

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What are the most reliable ways to create accurate data visualizations?

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1 Choose the right chart type

2 Follow data visualization best practices

3 Check your data quality and accuracy

4 Seek feedback and iterate

5 Be honest and ethical

6 Keep learning and improving

7 Here’s what else to consider

Data Visualization

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What are the most reliable ways to create accurate data visualizations?

1

2

3

4

5

6

7

1 Choose the right chart type

2 Follow data visualization best practices

3 Check your data quality and accuracy

4 Seek feedback and iterate

5 Be honest and ethical

6 Keep learning and improving

7 Here’s what else to consider

Data Visualization

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