Enhancing or Misleading the Audience’s Understanding of Data through Data Visualization

Truncated Y-Axis

The y-axis should range from zero to a maximum value that encompases the range of the data. However, there are data visualization designers that change the y-axis range to start at a non-zero value. This technique effectively highlights the differences in the y-axis values while, unfortunately, making the data seem much larger than they actually are.

Simplifying the Display of Data

In some cases, it is necessary for multiple sets of data to be compared with each other in order to highlight the differences in their trends and values. But in other cases where the data visualization designer wants to highlight the similarities between the sets of data, the data visualization design can be minimized to have a single set of data. In that case, the designer can include a note under the graph saying that the same observations hold true between all data sets.

Balancing Information in Visuals and Text

Background text should not be cluttered with too much important information that can be incorporated into data visualization designs, and vice-versa. That way, both the background text and data visualization designs are concise and compliment each other in order to effectively communicate the subject.

Excluded Information

Some information can be excluded if it does not help the data visualization design communicate the subject. However, there are instances where information that has been excluded makes the data misleading. For instance, a graph that is only labeled with the data “2016” will make an audience believe that the data was collected throughout the entire 2016 year when it was actually collected between March and May of 2016.



Works Researched

“Bar Chart.” The Data Visualisation Catalogue. N.p., n.d. Web. 20 Nov. 2016. <http://www.datavizcatalogue.com/methods/bar_chart.html&gt;

“How to Lie with Data Visualization.” Heap Analytics. N.p., n.d. Web. 20 Nov. 2016. <http://data.heapanalytics.com/how-to-lie-with-data-visualization&gt;

Klanten, Robert. Data Flow 2: Visualizing Information in Graphic Design. Gestalten, 2010.

Knaflic, Cole N. “Bar Charts Must Have A Zero Baseline.” Storytelling with Data. N.p., 20 Sep. 2012. Web. 17 Nov. 2016. <http://www.storytellingwithdata.com/blog/2012/09/bar-charts-must-have-zero-baseline&gt;

Knaflic, Cole N. “Improving Upon “Good Enough”.” Storytelling with Data. N.p., 19 July 2016. Web. 17 Nov. 2016. <http://www.storytellingwithdata.com/blog/2016/7/12/improving-upon-good-enough&gt;

Pradhan, Archana. “Comparing Mortgage Credit Variables by Applicant Age: Millennials have the lowest credit scores, and highest LTV and DTI Ratios.” CoreLogic. N.p., 24 July 2016. Web. 17 Nov. 2016. <http://www.corelogic.com/blog/authors/archana-pradhan/2016/06/comparing-mortgage-credit-variables-by-applicant-age.aspx#.WDMh5vkrJPb&gt;


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