Examples of displaying bad data | Examples of displaying bad data

Introduction

Data visualization is a technique used to transform data (in numerical form / of text) in an image that can be easily interpreted by the audience. Used to communicate to drive action, to inform or even to entertain, but most of the time it is used to persuade. It's easy to convince people by visualizing data.

The critical part is when convictions They were accompanied by the intention to deceive your audience. In this article, you will be provided with five (5) misrepresentations of data to be avoided at all costs and some tips and solutions to address those concerns.

(1) Using the wrong chart

There are graphics / boards that look good at first, but they give a misrepresentation of the data and would only confuse your audience. In a television news report from FOX in a presidential race of 2012, showed a Pie chart which seems off as shown below:

Using a pie chart should represent the data as part of a whole, which means that the sum of all the data in a pie chart must be equivalent to 100%. Contrary to a representation made by Fox News, the total percentage of the pie chart displayed is 193%. This could be very troublesome if it is displayed without the label. Would seem convincing.

When comparing data, the best way to represent them is through a bar chart As shown below:

In this new graph that represents the Presidential Race of 2012, the category Back Palin has the highest percentage. The proper use of a graph to represent our data will make it easier for the audience to know the message you are trying to convey..

(2) Eliminate the baseline or adjust the Y axis

The graph above is shown as if Back Palin has an overwhelming advantage against Back Huckabee Y Back Romney when in reality, it's just around 7% a 10%. To solve this problem, always start your Y axis with a baseline and use the proper scales.

(3) Go against the conventions

When submitting your data, the rules must be observed. As shown in the above graphic, we try to represent the larger data with a lighter color, but what is accepted in society is to use a darker color to highlight a specific part of the graphic. Imagine a more complex chart, as a geographic map and a heat map, and we try to change the conventional way of presenting the heat area / dense, which would lead to misconceptions and confusion. To solve this, always check the social norms in the area or with your audience when preparing your visual data.

(4) Untagged graphic and (5) Overloaded data

The graph shown above is also a poor representation of the data. First, it has no labels. Secondly, no specific data stands out. Finally, the other data behind the bars high in the cover information is shown in other bars. If no labels are displayed and large data is displayed in a graph, your audience would be confused and it would be more difficult to identify your point or the message you are trying to communicate by visualizing data. Over time, will explain it verbally and the purpose of presenting data through data visualization will not be served.

Then, what should we do?

In data visualization, we must also remember the 4 questions:

1. What data is important to show?
2. What do you want to emphasize in the data?
3. What options do I have to display the data?
4. Which option is more efficient to communicate the data?

The data is very extensive. As a professional data visualization, you only need to choose the essential data to display to your target audience. After which, highlight or emphasize the information you want to convey by changing the other data colors to gray or faded colors.

By displaying your data and knowing what is the most effective way to communicate your data, the chart below will surely help you decide:

ABSTRACT

To emphasize the most important data. Guide graphics for readability. Organize graphic / table. To avoid graphics overload.