There are many times where I have been asked to furnish an explanation of the utility of data visualization, and what its goals are. I’ve recently read a blog post by David Robinson where he describes the difference between data science, machine learning, and artificial intelligence. It’s a great post that distills the difference down into simplistic terms, then explains some of the nuances between them. In short:
- Data science produces insights
- Machine learning produces predictions
- Artificial intelligence produces actions
I’d like to suss out the role of visualization in the “data science” process, particularly its role in producing insights, which can be a wildly mis-used term in practice. Other researchers (Robert Kosara) and practitioners ([…who?!?]) have taken a stab at this, and other onlne communities (e.g, /r/dataisbeautiful) have adapted these definitions.
I’ll take the stance of what data visualization isn’t, and present three ideas of what I believe it is.
- To answer a specific question, you don’t need a data visualization.
- A data visualization supplies context
- A data visualization is a high-bandwidth connection to the brain
- A data visualization is informative
The rationale for these definitions is to delineate why one would use or create a data visualization, and to clarify how the design decisions that go into a data visualization can influence what a viewer can take away from a visualization.
To answer a specific question, you don’t need a data visualization.
If what you’re interested in is a specific metric or a specific aggregation, you don’t need a data visualization. There is a cost to create a data visualization: it takes time to make, and can be time-consuming for the viewer to comprehend (especially if they have to learn a chart type they have not encountered before). A number returned from a SQL query or a simple set of summing aggregations may be good enough.
There’s probably a reason why many dashboards for business and administration use so-called big-ass numbers in their presentations. The rationale for this is likely simple: these are the factors that are continually monitored, and the viewer has a good idea of what the numbers represent. For someone else that is looking at the number without context—it’s a little more confusing. This brings us to the next point…