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The Differences Between Good Data Visualization and Bad Data Visualization Part 1

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Last time, we discussed the importance of data analysis. This week, as we promised, we want to show you the best charts for visualizing different types of data by taking you through examples of some use cases. Many don’t know the best type of chart to visualize some types of data so, sit back, relax, and digest this very brief, eye-opening article on the differences between good visualizations and bad data visualizations

What is data visualization?

Data visualization is the representation of data or information in a graph, chart, or other visual formats. It communicates the relationships of the data with images that allow trends and patterns to be more easily seen.

What is a good data visualization?

A good data visualization tells a story to the audience, usually in images, graphs, or charts using language and ideas that they understand. Good data visualizations as the name implies have good data, it has a good choice of data visualization, the color or information are simple and explicit, the data are accurately represented, and there is consistency in scales.

Some of the ways to know or create a good visualization are by choosing the right choice of data and by also making sure the color or information of our visuals are simple and explicit, we can learn more about what makes a good visualization and start applying examples given to help make our visualizations better than it used to.

One of the main purposes of data visualization is storytelling, to lead the reader to understand what, along with the why visually. Insights cannot be drawn in a vacuum or by slapping a few charts together. Engage the reader and show them why they should pay attention or take action.

An example of data visualization storytelling can be seen in the chart below. The chart tells a story of how the total amount spent is distributed according to the categories of what the money was spent on in thousands. It’s clear that more money goes into the salary department than the rest of the departments. From our previous digest where we talked about the commonly used charts for data analysis, we explained that the bar chart is more suitable for displaying and comparing a number, frequency, or other measures for different discrete categories of data.
The story being told in the chart is clear enough that anyone seeing the chart for the first time can understand the message the chart is passing across.

The line chart below is also telling the same story as the first but line chart is not the best chart to tell this kind of story properly as we explained in our previous digest about line chart that it is best for showing trends and it is used to represent data over a continuous time span. So, the line chart is not the best chart to tell this type of story which makes it a bad visualization.

Even though it has been said that using different colors helps to make data visualizations quicker, using too much colors can confuse the viewer. It is important to choose a limited number of colors that are distinct from each other. At least 5 different colors should be the limit to using different colors for data visualizations.

The below visualization looks a bit messed up and cannot be said exactly which has a higher percentage than the other at the very first sight of the chart, apart from a few of them with very distinct sizes.

The same underlying data can be seen in the below chart and with a better view and here, the charts are clearly visible to know the Start Area with the highest sum to the lowest sum of distance unlike that of the pie chart.

Since we are still on pie charts, it is pertinent to avoid using pie charts in the manner displayed below.

Pie charts are inappropriate for displaying change in certain quantities over time or showing trends over time. In the visual below, the different contributors to a country’s gross domestic product (GDP) are being analyzed over time from Q4 2017 to Q1 2019. The pie chart being applied here makes it difficult to understand the relative changes and actual proportions of change in the quantities being measured – Education, Agriculture, Manufacturing, Information, and communication, etc.

A more appropriate chart for this would be – you guessed it, right? – Line Chart, to visualize trends over a period of time.

What is bad data visualization?

While bad stories may lead to bad memories, bad visualizations of your business data can lead to wrong conclusions that can hamper your business.

Bad data visualization is the complete opposite of good data visualization. It has bad data, wrong choice of data visualization, too much color or information, misrepresentation of data, and inconsistent scales.There are several mistakes many of us make while visualizing our data, some of these mistakes are made by making the wrong choice of data or by adding too much color or information to our visuals which makes our visual look too busy for anyone to understand the information being passed across. These examples are the common mistakes of bad data visualizations and many are guilty of this.

Next week, we will continue from where we stopped

Check this space next month for the Power BI Update for July 2020


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