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I plan to add another example to compare the density of data contained in different types of charts. However, in that case you are picking chart type based on what best shows the relationships contained within the dataset; optimizing the data-ink ratio is secondary to that, because it doesn't matter how high or low your data-ink ratio is if the chart type used is not the best one for your use case.

Yes, it would. And if you use black paper, then it is the white ink (I assume you use white ink) that you need to calculate for the data-ink ratio.

Just out of curiosity, what do you use black paper for?

For sure! It is all about context of use and the audience. I think the pie chart also serves its purpose in the right context and in front of the right audience. I would probably write a much longer article using different charts to illustrate all the various contexts but I don't want to be writing a textbook in the era of short attention spans. In this case, picking a bar chart to illustrate some principles clearly isn't going to satisfy everyone :p

But I will perhaps add a couple more examples based on all the comments and feedback so far. If nothing else I certainly enjoyed getting more people thinking and talking about how we can improve data visualization design!

This is actually a great paper to read (and as you say, quite recent for this type of review), and it does touch on the topic of "How to Design a Perceptually Efficient Visualization", which I think is most relevant to what an optimal data-ink ratio will provide for the audience (i.e. clarity and reduced amount of clutter). However, the only mention it makes about data-ink ratio (it doesn't do this directly) is that:

"Despite strong calls to declutter visualizations (e.g., Tufte, 1983), there is only mixed evidence that this practice improves aesthetic ratings and little evidence that the prescription affects objective performance. Several studies have measured aesthetic ratings for cluttered versus decluttered charts, and some have shown clear preferences for decluttered versions (Ajani et al., 2021) and others, surprisingly, showing the opposite (Hill et al., 2017; Inbar et al., 2007). Researchers who have found the opposite have typically argued either that viewers’ higher level of familiarity with cluttered charts make those charts more attractive or that decluttered charts that are too minimalistic become boring. Another possibility is that users may prefer particular depiction styles for particular purposes, mindful of their audience and goals (Levy et al., 1996). Objective performance measures, such as the speed with which viewers can compute means across values in a bar graph, also present mixed evidence. For example, that speed can be slightly faster when some forms of “clutter,” such as axis tick marks, are removed but slower when other elements are removed (Gillan & Richman, 1994). Given the large number of design elements that could count as clutter, combined with the large number of tasks that one could complete on a visualization, some have argued that a simple rule for whether to declutter is unlikely to arise and have discouraged further empirical testing given the small and mixed effects found so far (Ajani et al., 2021)."

I am certainly not arguing for the fact that the data-ink ratio is a magic number, but quite the opposite - I believe that even trying to maximize or minimize a ratio is full of dangers and traps, but instead we should understand our design decisions and be aware of its impact in a more considered and objective way. The discussions here suggests people don't believe it is a magic number, but it also shouldn't be discarded when it does have some value.

I think what you said about 'trying to optimize it' is actually on the spot. I think what too many people try to do is to 'maximize' or 'minimize' it without really understanding the impact of designing based on metrics. But I think what we can do is use a metric to understand some of the impact of our design decisions, and help clarify why it might make sense to do something. Even the best designers will know when it makes sense to break rules (because there are often good reasons to do so).

You are right in that there is a lack of empirical evidence in how to apply the data-ink ratio to design. This is one of the reasons why I am trying to sift through the previous research that makes claims without really testing them properly, so that I can hopefully make better sense of it with better research.

I think your point illustrates that optimizing data-ink ratio is not as simple as the concept of data-ink sounds, when it comes to best practices for data visualization design.

If you want to encourage people to compare bar heights, you can certainly leave the grey horizontal lines. But if you directly labeled the bars with values then you don't need to scan from the vertical axis across individual bars to do that processing in your head. And if you removed the vertical axis labels that don't represent values being charted while also sorting the categories by their value, you can further remove another step in the process.

The point is that you can try to optimize in either direction of the data-ink ratio, but you should be aiming to just blindly increase or decrease it. Often it ends up making very little difference to the actual ratio value (which relates more to information density), and you need other design techniques to improve clarity and reduce clutter.

There is a distinction between non-data-ink and redundant data-ink that is being made here. Whereas non-data-ink is usually easier to identify, redundant data-ink is somewhat subjective to how we view the information and process it. But you can argue that something is redundant if removing it doesn't change the amount of information contained within the chart that is required to understand and interpret it. However, you could also argue that different people interpret things differently, and therefore having different ways to do that will have some use.

Rather than trying to create unnecessarily debate, the intent is to try and help people understand the metric beyond a simple value but how the concept can be applied.

More importantly, it is not about simply chasing a perfect value that doesn't exist, but to optimize it within a specific context for your audience.

I wrote the article thinking that I won't need to explain some of the points explicitly with examples that are going to be subject to some criticism. But of course people who read the article asked for that and so I tried to illustrate some of the points as best as I can, but it is hard to please everyone. Nonetheless, I prefer to try and make a point for the sake of moving towards clarity rather than to continue circling in a place that I am not so happy with at the moment.

When I saw the UX Research Tools Map 2026 by User Interviews for the first time (I think 2025 was a fantasy map of some sort, a lot of effort spent on it for sure), something that I predicted when I saw the Subway Map to Agile Practices seemed to have come true...

How did you come across this book? Sounds very interesting... I'll have to check it out!