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What is Data Visualization?
Data visualization is a technique to present data in an easily understandable manner using visuals such as charts, graphs, and diagrams. This is a crucial practice as it makes the data more meaningful and actionable for a large range of users.
The purpose of data visualization is to identify trends, patterns and outliers in large datasets.
There are a variety of visual tools such as:
Graphs: Histograms, Scatter plots etc.
Charts: Pie charts, Donut charts, Bar charts, Line charts, Area charts etc.
Maps: Geographical maps, Heat maps, Tree maps etc.
Dashboards: Interactive combination of multiple visualizations
Importance of Data Visualization
Data visualization make the data more accessible and comprehensible especially when the data is large and the volume of information is overwhelming without appropriate visualization techniques. This results in more data driven organizational processes.
Data visualization is also very useful to present information to outside parties such as stakeholders, investors, media and so on.
Let us understand this with the help of an example. Using line charts, we can show the profit trend of a company for 10 years where we see the line moving up when the company incurs profit and for the year it incurs a loss, we see a dip. This information cannot be obtained by just looking at the data table.
Data visualization enables to represent the trends in data which cannot be observed by looking at a data table, it provides a perspective on data by showing the larger scheme of things, it saves time by providing a clearer insight in a shorter span of time, and it aids in data storytelling which enables quicker and easier decision-making.
What happens if there are mistakes in Data Visualization?
Data visualization is prone to human errors and poor data visualization can lead to bad decisions as it will not be possible to spot trends and patterns effectively and thus lead to an inability to make correlations.
Signs of Bad Data Visualization
Insights are not highlighted.
Too much information is tried to be shown.
Inappropriate scales or measures that misrepresent the data.
Confusing data description which doesn’t match the data.
Most Common Data Visualizations and How to Avoid Them
Selecting the Wrong Chart Type
Choosing an appropriate chart type is crucial to show information. Each data visualization type has a specific purpose and should be used to convey a particular type of information otherwise it will be difficult to understand the data.
Consider whether the purpose of the chart is to inform, show trend or compare information.
If the purpose of the visual is to inform, consider using a donut chart or pictogram.
Line charts, Area charts, Map charts can be used to show trends or patterns
Comparisons can be effectively shown using bar charts or pie charts. Tree Maps, Bubble Charts, Stacked Bar Charts or a Word Cloud can be used depending on the type of information and the amount of data that has to be shown.
Also, each chart has its own strengths and weaknesses which we need to consider. A pie chart, for example, can effectively show percentages and proportions, but it can be difficult to read and compare. A line chart is effective to show trends but can be cluttered and confusing. A bar chart can show comparisons effectively but can get repetitive.
The key is to choose the right type of chart depending on the data, the audience and the story.
Using Misleading Color Contrast
Another crucial factor in data visualization is to choose the right colors for your chart. If similar colors are used, the lack of contrast makes distinguishing between the different shades of blues, greens, oranges, reds etc. becomes difficult.
On the other hand, too much color or a lack of distinguishing color choices can slow down the reader and increase the time to understand the chart.
It is important to avoid using colors that are too similar or too contrasting.
Using Misleading Scaling
Scales are used to show magnitude, direction, or distribution of data. It can have a significant impact on how your audience perceives the data. It is best to avoid using non-standard scales that distort the data, such as truncating any axis to exaggerate differences or using uneven intervals. Baseline should start at zero for both the axis and contain data point spacing that is equal to the corresponding numbers
Cluttered Visualizations
Having too much information in a single chart is a common mistake in data visualization. Cluttered visualizations are those that include too many visual elements, such as multiple text boxes and graphic layers which lead to confusion for the audience. It is important to focus on what your audience wants to know. The solution for this is to have multiple charts, each of which tell their own story rather than have visuals that are too busy to be read and understood and read effectively.
Data Distortions
A distorted graph is one which represents data and leads to incorrect conclusion. Data may be distorted intentionally to hinder its proper interpretation or accidentally due to unfamiliarity with graphing software. Apart from being distractions in visuals, data distortions have the potential to mislead the audience. An example of distorted data is a bigger piece in a pie chart is representing a smaller number which may lead to false conclusions.
Cherry Picking Data
This involves selectively displaying data which looks favorable while ignoring evidence that contradicts it. It is a way of zooming on favorable data and only little insight from actual data will be shown in the visualization.
Not Using Clear and Consistent Labels
It is important to use clear and consistent labels for your legends, axes, annotations, and titles. Labels inform your audience what the data is about, what variables you are using, and what insights are being highlighted. Labels need to concise, informative, accurate, and they should reflect the tone and style of your story. Appropriate labels help to avoid ambiguity or confusion.
Using 3D Charts Ineffectively
3D Charts can easily distort the data as the angles and perspective might make some sections look larger than they actually are. This leads to misinterpretation of data.
Ignoring Design Principles
Using white space, contrast, alignment, and hierarchy can help to create clean, visually pleasing and elegant charts.
Too much noise in the chart
A common mistake when creating data visualization is to add unnecessary grid lines, colors, fonts, borders, backgrounds, or effects which distract the audience from the main message and make the chart difficult to read and interpret. This also reduces the aesthetic appeal of the chart. It is best to create simple and minimalistic charts, using elements that enhance the story and data.
Conclusion
It is vital to use proper data visualizations to enable your audience understand the numbers that you are representing. They make utmost sense when clear and accurate information is displayed through your visualizations and for this it is best to avoid the data visualization mistakes explained above.
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