The Manifesto of the Data-Ink Ratio
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 analyze massive amounts of information and make data-driven decisions. There are several things can contribute to distraction in data visualisation. These include:
- Use of 3D effects,
- Background images,
- Shadow effects,
- Unnecessary borders, and
- Unnecessary grid lines.
In his book The Visual Display of Quantitative Information, Edward Tufte, a leading authority on data visualisation, devised a formula for understanding the amount of distraction present in data visualisation. He called this formula the Data-Ink Ratio, which is shown below:
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. (Ratio of Data-Ink to non-Data-Ink). 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. An example is shown below where the background, grid lines, 3D effects, shadows and other unnecessary aesthetics distract from the data being represented.
And 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.
As can be seen, removing distractions makes the visualisation far easier to understand and the person viewing the visualisation 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 provided Five Laws of Data-Ink, which are:
- Above all else show the data,
- Maximise the data-ink ratio,
- Erase non-data-ink,
- Erase redundant data-ink, and
- Revise and edit.
So with this in mind, there are some practical things we can do to make a data visualisation easier to understand.
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 making them 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.
Ultimately, it is all about striking a balance, simplifying charts and graphs to the point where they are clear and understandable.
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