Data Visualization Checklist to make pro visuals in 2022!
Have you ever wondered how some visuals stand out in a bunch and can be visually appealing and at the same time, highly informative? In this short article, we’ll go over breaking down a Data Visualization checklist in order to take your visualization skills and go pro in 2022 in every visual you make. This may take a couple of extra minutes for you to check everything on the list, but the pros are unmatched!
Note: The checklist we’ll break down here is provided by Makeover Monday and I think it’s one of the best out there. Check out their website for more resources and webinars here:
Here is the checklist (if that's all you need and will skip the breaking down part of the article)
Let’s break it down now:
Title:
Now the title is very straightforward, and you can note the following things whenever you want your title to stand out (because well, before your visualization, everyone’s going to see the title)
- Not too many words
- Visible (large font size, contrast, not a very fancy font)
- At the top of the article, preferably at the center or just about anywhere where it will be visible enough
- * RELEVANCY *
Typos:
A simple grammar check in all your work and annotations will ensure you don’t leave a typo here or there for someone to point out. In some cases, it can change how people perceive your work. For example, if you have to suggest a z-score somewhere and you forget to add the ‘z’, it will leave your audience confused about what score you’re talking about there.
Punctuation and other mistakes are essential too. If you are describing something in your visual using a text box, plug it into a grammar checker like Grammarly and ensure you are using the right punctuation, spellings, grammar, and sentence structures. (Because communication is key to a good data presentation)
Colors and Contrast:
Keeping your Colors and Contrast on point is very important in any visual because if you are presenting your data to anyone, you don’t want them to look away because the colors are too bright or try finding things in your visual because the colors are too similar. A high-contrast visual is good when you don’t have a lot of data to show and you want your audience to focus on something particular.
Low contrast visuals are good for too many things to read on the screen because the low brightness of the background color will help the text become more readable as such. Find out more about contrast here:
Formatting:
Formatting a dashboard or a visual is one of the key things a data enthusiast can do to communicate precise and accurate information without giving the viewer a lot of unnecessary information that will only distract them from what is actually important in the visual.
To do this,
- Ensure your elements are evenly spaced.
- A visual needing a larger space should get it. Don’t cram it up.
- Don’t put too much noise in your visual.
- Focus on one or two key points you want the viewer to note and highlight them accordingly.
Neatness:
Now, most articles and data viz software like Tableau, Powerbi, etc. are very helpful and in order to ensure one understands the graph clearly; they come with some extra layers on the graphic like gridlines, droplines, reference lines, and too many other lines at times.
This calls for a clean-up of some unnecessary elements and layers as per your use case and you can choose the level of neatness you need in your dashboard.
You can remove any additional markers, droplines, gridlines, axes, shading, labels, legends, etc. that you know will not add any value to the viewer.
Annotations:
As previously mentioned, try adding a couple of annotations to ensure the viewer focuses on those particular elements from your dashboard or visual. You wouldn’t want something amazing you noticed in your data viz to go unseen right?
Annotation of a point or area, just shading that one area you think is important, using data storytelling, etc. will help you take your visualization to the pro leagues.
Find out more about dashboarding and data storytelling here:
Tooltips:
Tooltips might be very original to only data viz software like Tableau, PowerBi, etc. but can impact your visual in a very good way. The reasons are very simple; they save time and they can communicate more information than the visual itself at any given point on the graph.
Make sure your tooltips are neat and don’t have unnecessary information and can show your viewer exactly what the data point is about. This will make a difference in datasets where there are outliers in the data or in a scatterplot where one particular point has been annotated for observation. A tooltip will only explain the data point in a better way and is always a good thing to add.
Credits:
This point is very straight. If you are using someone else’s work or ideas, give them the credit they deserve. Collectively, the knowledge of multiple brains put together is always going to make something remarkable. This calls for everyone to give due credit to someone for their work being used and taking permission to do so whenever needed.
Note: Give credit where due.
Test Run:
If your data visualization has interactive elements like filters, highlighters, slicers, animations, hyperlinks, etc. give it a minute to touch upon each of them to ensure that they are clearly visible and functional. If your interactive elements don’t work, they call for questions about the visual, and important data pointers can be missed out, which is best if avoided.
Try adding some more points in the checklist that you think are things we all should do when making a data viz. Drop it in the comments below for everyone to see and leave a clap if you liked the article! Thanks for reading it all the way here and I hope the checklist helps you in your artwork with data!
Also check out my previous articles here, if they interest you!
For more such articles, stay tuned with us as we chart out paths on understanding data and coding and demystify other concepts related to Data Science. Please leave a review down in the comments.
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