Have you ever looked at a chart or table and found that something in the way it was designed was distracting or hard to understand?

Data visualisation is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualisation tools provide an accessible way to see and understand trends, outliers, and patterns in data. In the world of Big Data, data visualisation tools and technologies are essential to analyse massive amounts of information and make data-driven decisions. There are several things that can contribute to distraction in data visualisation. These include:

What is the manifesto of the data-ink ratio?
The data-ink ratio is the proportion of ink that is used to present actual data compared to the total amount of ink (or pixels) used in the entire display.

Good graphics should include only data ink. Non-data ink is to be deleted everywhere where possible.

Of course, when applied to the digital world we are not really talking about ink – but the principle still applies. Using the formula above:

Data ink represents all of the minimal elements of a diagram that are required to represent a set of data
Total ink represents all of the elements used to create the entire diagram (including aesthetic elements).
When quantifying the data-ink ratio of a data visualisation, the closer the ratio is to 1, the less distracting the visualisation is considered to be.

Examples of high and low data ink
If a graph has too much noise and distracting elements, it is considered to have low data ink. In the below example, the background, grid lines, 3D effects, shadows and other unnecessary aesthetics distract from the data being represented.

When a graph shows the minimal graphics required to represent data, it is considered to have high data ink. An example is shown below where all of the distracting elements have been stripped away to the point where the visualisation is clear and it has maximum focus on the data.

Removing distractions makes the visualisation far easier to understand and the person viewing this is able to focus more on the data.

However, we need to make sure that a diagram is not simplified so much that the ability to understand the data is reduced. So, how can we do that?

Tufte’s five laws of data ink
Above all else, show the data
Maximise the data-ink ratio
Erase non-data ink
Erase redundant data-ink
Revise and edit

Practical considerations
Ultimately, it’s all about striking a balance, simplifying charts and graphs to the point where they are clear and understandable.

The data needs to be the number one priority. Adding anything else to the visualisation should be considered carefully. If adding more to the visualisation is not going to improve its understanding, then it should probably not be added.

Look at your diagram and see if anything can be removed. Are there grid lines that are competing with the data? Remove them, make them thinner or lighter in colour, compared to the data visualisation.

You may be surprised how many graphs or charts can still be understood without grid lines. There may be exceptions to this rule though, so take this on a case-by-case basis.

The manifesto of the data-ink ratio

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