Understanding Visualization Genres
Forgive me: this is a bit of a rant. To make sure it’s a good one, we’re doing it in five parts.
Where and how will this visualization be used?
This question drives every choice made when designing a visualization – from the design of the chart itself, all the way down to the choices of how the data architecture needs to be arranged. The creator of the visualization must make different choices depending on the use case and context.
I think of this in the same way I think of “genre” in writing. You wouldn’t mistake a memoir for a news article or a technical manual – they use different ideas of what to include, different writing styles, even different choices of words. Even though all three might be written with clear, grammatical language, their goals are different. A news article strives for emotionless facts, while a memoir might be more informal. Different occur in different contexts.
From a reader’s point of view, understanding the genre of a piece of writing will be a critical key to understanding how to interpret it.
The same is true of visualizations. Different visualization genres suit different purposes. The choice of visualization genre – the purpose, audience, and context of the visual – helps us think about appropriate visualization techniques.
I’d identify three major genres of visualization, each of which comes with its own questions:
Exploration: I want to discover new insights about my data.
Presentation: I know the answer and I want to share it with others.
Monitoring: I know the questions I want to ask, and check them from time to time.
From left to right: a python notebook showing Exploration; Hans Rosling showing a data presentation; a dashboard showing data monitoring
Understanding the genre of a visualization is incredibly important. A designer will make very different choices if they expect the question they are asked to have a factual answer — or whether they are instead presenting a visualization to be featured on the front cover of a newsletter.
Yet somehow this question gets skipped over far too often. At Microsoft, colleagues would ask me to help them learn about a dataset. They would be disappointed that the result was unlikely to be a novel data representation with a cool 3D animation. Instead, I’d present them with a data exploration in Excel or Python, neatly identifying insights and clarifying results.
We’ll come back to my colleagues’ disappointment in the last entry. In the next few, I’ll go a bit deeper into each of these genres, exploring their unique characteristics, challenges, and best practices. By recognizing and embracing the concept of genre, we can figure out what to build, what to expect, and who will use our charts.