Data Viz Principles
Good data visualization is more about what you mustn’t do than what you must do because it’s a creative endeavor that you wouldn’t want to restrain too much. These are common mistakes you must avoid…
- The wrong type of graph:
- The specific type doesn’t match your use case adequately.
- Using cumulative graphs when the data isn’t cumulative in nature
- Too many or unnecessary elements:
- Using too many graphs at once and especially when not related to each other.
- Using elements that have an aesthetic purpose but none or a limited functional one serve to distract from the message
- Incorrect scaling and/or limits/bounds:
- Use axis scales that don’t represent your data in an intuitive manner (not following conventions)
- or worse using cropped axes which can be misleading and dishonest
- Issues with clarity:
- Inadequate or non-existent annotations
- Overlapping labels
- Difficult to understand aspect ratio
Ultimately the goal is to maximize what is communicated by the visualization but also minimize its cognitive strain. To this end, succinct communication is usually preferred.
By: Serg Masis