Data Visualization

Turning numbers into marks a person can read in a few seconds.

Also known as: data viz, dataviz, charting

Data visualization is turning numbers into marks a person can read in a few seconds. The point is never the picture; it is the comparison the reader can finish without counting.

Why it matters to you

Your reader is a stakeholder who asked one question and has limited attention. A chart answers “which region fell behind” far faster than a forty-row table answers it. It is also the fastest way for you to find out that the data is not what you assumed — a flat line where you expected seasonal movement is information you would never see in a total.

Three jobs a chart does:

It shows you the shape before you summarise. Exploratory data analysis is mostly looking.
It makes a comparison in one glance that a table would make a reader compute.
It makes the honest version of the finding easy to see, and the dishonest version harder to hide.

The classic mistake

Charting the number you have instead of the comparison you were asked for. “Monthly revenue” is a chart. “Monthly revenue by region, last thirteen months, against target” is an answer. If you cannot say which comparison the reader is making, the chart is not ready.

A second common mistake is starting with the chart type. Start with the question and let the question pick the form — chart selection is the map. The numbers underneath still have to mean the same thing everywhere, which is metric definitions, and most charts end up inside a dashboard or a story rather than in a file nobody opens.