I often work on projects that need to go 'fast'. On day one, I immediately get overwhelmed with tasks to create countless visuals that express certain KPIs, ratios, indexes, and so on. Ideally, the stakeholders want to see them all on one page as an 'eye-catcher' and want to be able to filter on everything and nothing. When asking for a clear mockup, only a piece of draft paper is available with some scribbles. To top it all off, the business expects to see it done by tomorrow.
Sounds familiar?
Well, you are not alone in this. Companies that tend to ask for 'fast' deliverables without the visualizer knowing anything about the data are the same as asking a bus driver to fly a plane (it works with a throttle and a brake, right?). When this happens as a client, for me this is the time when I often ask to take a step back and let the data 'breath' for a little.
Without knowing what the data is about, for me, as a visualizer, it will be difficult to come up with insightful visuals without even looking at the available data. Have the courage to dare to take a step back to get an overview of everything, interview the business, and ask the needed questions. At the end of the day, you will have achieved more than a trial and error approach. This also has to do with the data visualization process, which is often underestimated at companies. So if you like to know what the process is all about, read further.
Three phases
From my point of view, the foundation of data visualization is to facilitate the process of understanding the data. To understand the data, the viewer will go through the process which is split into three phases: Perceive, Interpret, and Comprehend. Each phase has its specific impact in the process, where one phase is more controlled by the visualizer, or a phase that is controlled by the viewer itself. Let's take a look at the three phases.
Perceiving
The first phase is perceiving, where the focus is on reading the chart. Questions that should be answered in this phase are:
What do I see?
What data is shown?
How is the data represented?
Is the data trustworthy?
No additional context is needed to know what you see.
Let's have a look at the following clustered vertical bar chart and perceive the chart:
I see a clustered bar chart that is vertically oriented. It is showing quantitative values of categories for a specific year. I can derive a lot of information from the title. I feel at ease because the chart feels familiar and easy to read.
When scanning the chart, my eyes are drawn towards the first category, as this category seems to be very dominant. The total sales are 3.32 million dollars, which is a lot more than other categories combined.
The source seems to be Kaggle, which is a data science platform site that presents datasets to the open public. These datasets are often official, so my feeling is that we can trust the data.
Interpreting
Interpreting the data is different from the first phase in the sense that additional context is needed to digest what is going on. In this phase, the observations from the perceiving phase are translated in a quantitative/ qualitative meaning.
Which features of the visual are interesting?
What is unexpected?
What features are important?
A viewer's ability to interpret a visual will be determined by factors that are external to the visualization itself. The viewer needs to have a connection to the subject to be able to interpret it.
"We can look at the data, but sometimes we cannot really see it"
In this phase, we can also look at the factor of willingness. Not everyone has the same inclination to engage with a visual. Some people are not interested or the visual has no immediate relevance to their business needs. Think of CEOs that need a high-level visual, where operational employees would need to be able to see a more fine-grained visual.
Let's look again at the global sales graph of gaming platforms to interpret it:
My interest in gaming helps me to interpret the chart. For instance, handhelds are incorporated as well in the chart, being one from Sony (PSV) and Nintendo (3DS). When looking at these two values, it seems that Nintendo managed to sell more games on their handheld than Sony. ($0.62M vs $0.31M)
The absolute winner of the sales is the PS4, towering above all the others and influencing the average a lot. Only two of the eight categories perform better than the average of $0.79M.
I also see that X360 games are still sold, while the XOne console had already been released for around 3 years. The same goes for PS4 vs PS3 sales. This could be interesting for the companies to know that there is maybe an urge to have backward compatible games. (sidenote: they have done it for the PS5 and Xbox series X)
Comprehend
The last phase is the comprehension phase. This phase leans closely to the interpreting phase, where the difference lies in the fact that the interpreted facts are reflected on the viewer himself. In other words, the viewer tries to seek answers to what the visual means to himself.
What have I learned?
What do I feel?
What do I do now?
Let's comprehend the chart, which will conclude the exercise:
In my specific case, the outcome of the understanding process is nothing astonishing. With the help of my interest, I knew that Playstation and Xbox were two top-selling gaming platforms. What I did not know is that Playstation is towering above all the others and dominating the market. Another interesting fact is that PC games are not nearly as big as the consoles and that even PS3 sales outperform the PC. This could impose on the fact that maybe a lot of games were downloaded illegally as a lot of games are played on PC.
In any case, the outcome of the understanding achieved from each visual is depending on the viewer itself. One person's 'wow' is another person's 'I knew that' is another person's 'I don't care'. The visualizer cannot always anticipate what the audience knows and what not.
Conclusion
To facilitate the process of understanding data, a viewer undergoes three phases: perceiving, interpreting, and comprehending. With each phase having its impact on the viewer, it is still sometimes difficult to get multiple viewers on the same page with one visual. Visualizing data can be seen as an agent of communication, and not a guarantor for what the viewer does with the opportunity for understanding what is presented.
Source: Kirk, A. (2019). Data Visualisation (2nd edition). SAGE.
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