In class and while working we have come across many ways of making an effective visualization. The visualization can be made more effective by keeping in mind few simple points-
- Displaying too much data: It is extremely important to not confuse the reader with bombarding of information. The data must be precise and correct. It should be easy to summarize for the reader.
- Oversimplifying data: Complex data should not be oversimplified for visualization purpose. This can change the entire meaning of the data and the reader might interpret it in a wrong way. Hence oversimplification of data must be avoided.
- Choosing wrong visualization: The designer must understand which king of chart would be useful for which kind of data. The use of pie charts and donut charts must be completely avoided as they give the wrong interpretation of information. Also 3D techniques must necessarily to make the visualization more appealing.
- Not following conventions: It is necessary to follow correct conventions while designing any visualization. The x and y axes should always be present while quantifying the data. Correct annotations must be used wherever needed. The graph should always be labelled to understand the correct meaning of it.
- Don’t use hard to compare data: While creating visualization it should always be kept in mind that is there is a comparison, it should be of similar nature. Non related data must not be used for comparison as it does not make any sense.
By following these simple steps an effective visualization can be achieved.
Reference: http://www.techadvisory.org/2015/07/data-visualization-mistakes-to-avoid/
http://designroast.org/the-7-most-common-data-visualization-mistakes/
This are helpful guidelines. Need to abide by them when we do our visualizations. 🙂
Thanks! yes we should remember them.
This post was a great refresher to the class discussion on “Rules of Thumb”.
The labeling of the graph and giving correct annotations is more important for your audience, since you would have already analysed and interpreted the underlying data in a particular manner but your audience may see for the very first time.
Yes Sangamitra! Annotations help in clearing the meaning behind the visualization.
This post was a great refresher to our class discussion on “Rules of Thumb”.
Following the correct conventions and giving the right labeling is more important for the audience since you have already analysed and interpreted the data in a particular manner but your audience are viewing your work for the very first time.