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What techniques can you use to optimize data visualizations for print?

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Data visualizations are powerful tools to communicate complex information, but they can lose their impact if they are not optimized for print. Printing data visualizations involves different considerations than displaying them on a screen, such as color, resolution, size, and layout. In this article, you will learn some techniques to optimize your data visualizations for print and make them more effective and appealing.

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Aurélien Vautier

I Help You Master Dashboard Design.
Sanvee Jalan

Insights professional at EY!
Danyal Huq

Strategy and Performance at Kavak

1 Choose appropriate colors

One of the most important aspects of data visualization is color, as it can convey meaning, contrast, and emotion. However, not all colors work well on print, especially if you are using a low-quality printer or paper. Some colors may look dull, distorted, or indistinguishable on print, which can affect the readability and accuracy of your data visualization. To avoid this, you should choose colors that are compatible with print, such as CMYK (cyan, magenta, yellow, and black) colors, rather than RGB (red, green, and blue) colors, which are designed for screens. You should also avoid using too many colors or colors that are too similar, as they can create visual clutter and confusion. Instead, use a limited and consistent color palette that matches the tone and purpose of your data visualization.

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Sanvee Jalan

Insights professional at EY!
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Use the same family of colors or palette throughout the deck helps maintain consistency and provide intuitive understanding to the readers. Using the same legend across visualizations (wherever possible) is a good practice.

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Danyal Huq

Strategy and Performance at Kavak
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Appropriate color emphasizes what you want readers to see. This means ensuring color is used intentionally: to highlight a data point or series, to rank order importance of key messages, or making particular data points or series easier to see. Color is arguably the most important decision to draw readers' attention to where you want it. I recommend using Adobe Color Wheel for selecting appropriate colors and judgment on how to apply them.

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Dr. Rajan Thapaliya, Ph.D.

Founder & CEO of Data Jupiter || PhD's Dissertation Chair, SME and AR || Professor of Data & Computer Science at the MS/Ph.D. level courses || Principal Data Scientist at Big Dreams || MS/Ph.D. in Data Science ||
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Color is a crucial element in data visualization, conveying meaning and emotion, but it's essential to consider its suitability for print. Low-quality printers or paper can render some colors poorly, affecting readability and accuracy. Opt for print-friendly colors like CMYK instead of RGB, which is designed for screens. Avoid an excess of colors or similar shades to prevent visual clutter and confusion. Instead, use a limited and consistent color palette that aligns with your data visualization's tone and purpose, ensuring it translates effectively to print.

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Hakeem Hussain (MCDA,MCT)

Senior PowerBI Developer | Microsoft Power Automate| Microsoft PowerApps |Microsoft Fabric
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Personally, this was a big deal in the past but after taking a few courses on UI/UX it made more sense now. I always work within the scope of my company's color scheme and when I work on other non-work related projects I make sure I never use more than 3 colors. It might also help to work with colors that fall into the same palette.

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2 Adjust the resolution and size

Another factor that can affect the quality of your data visualization on print is the resolution and size. Resolution refers to the number of pixels or dots per inch (dpi) that make up your image, while size refers to the physical dimensions of your image. If your resolution is too low or your size is too large, your data visualization may appear blurry, pixelated, or distorted on print. To avoid this, you should adjust the resolution and size of your data visualization to match the specifications of your printer and paper. For example, if you are printing on a standard letter-sized paper (8.5 x 11 inches), you should aim for a resolution of at least 300 dpi and a size of no more than 7.5 x 10 inches. You should also check the preview and print settings of your software and printer to ensure that your data visualization is aligned and scaled properly.

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3 Simplify the design and layout

A third technique to optimize your data visualization for print is to simplify the design and layout. Design and layout refer to the elements and arrangement of your data visualization, such as the title, labels, legends, axes, grids, shapes, and fonts. These elements can either enhance or detract from the clarity and aesthetics of your data visualization, depending on their use and presentation. To simplify the design and layout of your data visualization for print, you should employ a few basic principles. For example, use clear and concise titles and labels that describe the main message and variables of your data visualization. Additionally, only use legends and keys when necessary, placing them close to the data visualization to avoid confusion. Axes and grids should be used only when they help to show the scale and distribution of the data; avoid unnecessary or misleading ticks and labels. Additionally, simple and consistent shapes and fonts that are easy to read on print should be used. Finally, white space and margins should be used to create balance between the data visualization and the surrounding text.

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Aurélien Vautier

I Help You Master Dashboard Design.
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When preparing data visualizations for print, it's crucial to : - Refrain from using tooltips and filters. - Avoid interactive features like visual filters. - Limit the number of visuals to prevent excessively large pages. - Increase text sizes that are typically quite small for digital purpose. - Select colors that match the rendering of printers within the company. - Show labels that typically appear only when hovered over with the mouse. - Additionally, it's advisable to avoid complex visuals such as scatter plots and advanced charts, since they might generate questions that the static visualization can't directly address.

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Sanvee Jalan

Insights professional at EY!
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Data visualization is meant to not just show data, but a compelling story. Adequate labelling, legends and title are very important elements to create that story. Another overlooked element is highlighting the key takeaways on the visualization itself. E.g. A bar chart showing world GDP growth over the last 20 years will instantly depict the trend, but an insight on the reasons for extraordinary highs and lows will complete the story.

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4 Test and evaluate

In order to optimize your data visualization for print, you should test and evaluate it. This process involves checking and improving your data visualization before and after printing. Testing and evaluating can help you identify any errors, inconsistencies, or gaps in your data visualization, as well as assess its effectiveness and appeal. To do this, print a sample of your data visualization on the same printer and paper that you will use for the final version. Additionally, ask yourself or others questions about the data visualization such as if it is clear, accurate, relevant, interesting, attractive, engaging, suitable, and consistent with the purpose and audience. If necessary, make changes or improvements to your data visualization based on the feedback and criteria. Repeat the testing and evaluation process until you are satisfied with the result.

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Sanvee Jalan

Insights professional at EY!
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Peer review can be useful to gather a feedback and improvise accordingly. A fresh pair of eyes are the best to identify obvious errors and edits.

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5 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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What techniques can you use to optimize data visualizations for print?

1

2

3

4

5

1 Choose appropriate colors

2 Adjust the resolution and size

3 Simplify the design and layout

4 Test and evaluate

5 Here’s what else to consider

Data Visualization

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What techniques can you use to optimize data visualizations for print?

1

2

3

4

5

1 Choose appropriate colors

2 Adjust the resolution and size

3 Simplify the design and layout

4 Test and evaluate

5 Here’s what else to consider

Data Visualization

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