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Mistakes when using data visualization tools are common because we often work with 100 to 10000 data. You can forget to include Y or X-axis, or put too many visuals in the webpage or dashboard. It is familiar to data researchers, workers and analysts. Mistakes in data visualization leads to wrong data, bad decisions and loss of your credibility.
If you work with data and visualize them, you must ensure maximum data accuracy. A single mistake of digits, points or numbers can change the result. You have to start with your first data and check each axis, column and row in your visualization tool.
You would want to avoid time-consuming data rechecks. You should know the common data visualization mistakes for your work-speed, credibility and data accuracy. Focus on data insights, topography, color palette, and proper use of your dashboard and whitespace.
You might forget to input 0.001 in your visualization tool. Or, we often mistype the data numbers and their tables. Although these mistakes with data looks minor, it leads to inappropriate results.
You work for 1 to 10 hours, or more to visualize the collected data. And then, you find that, there are mistakes in your visualization. The color palette may be inconsistent, as you used red, blue, etc., colors wherever you wanted. Also, you discovered that you forgot to add the Y-axis.
These data visualization mistakes will kill your hours. You have to start inputting and visualizing the data from 0. So, you could lose time and resources due to mistakes in data visualization. For example, you have to use your desktop more to re-visualize the data. It increases your electricity bill 5% to 10% or more.
One or two mistakes to visualize your data is realizable. But what if you include erratic data more than three, four, five times? Or there are frequent errors in your data! You frequently include wrong data, errors in your data analysis and mistakes in your visualization of the data.
Such frequency of data visualization mistakes will lower your credibility. Clients will hesitate to give you new data analysis and visualization tasks. When you don’t get appropriate data visualization tasks, it hurts your career.
Also, at office, management will give you bad score for data visualization errors. At worst, frequent mistakes with your data will cost you your job.
You are calculating the cost of a 5KM bridge made with 30mm steel. Every input such as the length of the bridge, required steel, its diameter are important. A mistake of a single point (.) or digit, can alter the cost from 500-million to 5-million.
So, visualization mistakes to present data are never welcome. A single mistake in including the right table, chart or graph can cause millions of dollar or the life of many people. As a data visualizer, you must always be 100% accurate when highlighting the data.
80% of organizations and people reported that they don’t return to data visualizers with frequent mistakes. If you commit multiple mistakes in your data visualization, it shows your inattentiveness. So, people will think twice for giving your data visualization works.
You lose data visualization credit and clients. It also creates a bad impression among your data analysis and visualizing clients.
You work with fun and relaxation to visualize the data. But did you ever consider what happens if you make any mistake in showing the data? Also, what are the common mistakes you can make to visualize the data through graphs, images or videos?
Often, data visualizers will input too many numbers, tables, and datasets in the same page. It is tempting to feed as much data as possible in a single screen. But it may be impractical and ineffective to visualize too much data in the same screen.
When you input multiple charts, diagrams, or tables in a single screen, data becomes cluttered. People will find it hard to find the real data, numbers, etc. For instance, you might be working within the gross margin, gross revenue and productivity of Tesla, Apple Inc., and Microsoft.
You would like to show maximum data and visualize them in a single screen. Instead, you can show them in multiple pages and break down the data visualization in many parts. You can compare the gross margin of Apple Inc., Tesla and Microsoft in a chart. Then, go to the next page to show the comparison of their productivity.
Too Many Data Input
No Data Found
Nice and Clean
No Data Found
As you see in the two images, the first image has too much data and cluttered visualization. So, data analytics and people will find it hard to memorize all the data. But, in the second image, people can grasp the gross margin ratio of Apple, Tesla, Microsoft, Hammer at ease.
When you put too much data in a single chart or page, people experience cognitive overload. As a result, people will fail to understand your visualized data.
Excessive and overloaded data visualization happens for:
You should clarify which data is more important and which is not, at first. Also, keep your data visualization clean and simple. Don’t use too many charts, bars, or colors. Balance between your data visuals and whitespace to give relief to eyes.
The X-axis and Y-axis in your data visualization is crucial. The two axis scales help people realize the numbers, points, data, dates, etc., appropriately. But, if you don’t input the data for axis scales, it can mislead people. Also, without proper description of the axis scales, analytics may misinterpret the data you have visualized.
At worst, unclear and misleading axis scales cause bad decision making. People can manipulate your data visualization and use it for their profit. So, always ensure that you include proper description, scaling and interpretation of the axis scales.
Also, inaccurate axis scales lead to inappropriate conclusions, flawed decisions and distorted opinions. If the axis scales are not clear, people can exaggerate or diminish it.
The main mistakes with axis scales for data visualization are:
All these mistakes in formatting axis scales can make your data more or less important. Focus on similar formatting and scaling of both X and Y-axis. Also, use appropriate zero origins to avoid baseline errors in your data visualization. Always start the axis scales at zero and define the units and labels clearly.
Data isn’t just about numbers, ages, dates, etc. Similarly, data visualization isn’t about showing the numbers in your chart, diagram or chart. As a data analyst and visualizer, you must understand the context of the data.
For instance, you might be working with gross earnings, salary, home rent and food cost of New York City and Delhi. New York is a city with high income and expenditure. But, Delhi is in a third-world country and so, doesn’t have similar income and expenses like New York. If you consider the same data and visuals for both cities, you would ignore the context.
Relevant context for the data visuals is essential for accuracy. If you misunderstand the context of the data and its visuals, you ignore insights of the data. Also, you might miss opportunities to use the data visuals with the right steps.
So, you should include relevant context and information to visualize the data:
Define the data labels, scales, and units appropriately. Also, if necessary, explain the data visuals with proper description. Include drilldown and tooltips. It helps data analytics and people to explore the labels, units and diagrams of the visuals better.
For images and videos of data, follow a narrative style. Arrange the data like a storyboard to explain the numbers. People can visualize and realize the data better in storytelling format.
Data visualizers may choose wrong or inappropriate chart types to show the data. For example, many data visualizers might use pie charts to show data for the time series. But, pie charts are suitable to show the proportion of any data or percentage. You can use an area or line chart to show your time frames.
Data visualizers also use bar charts to visualize cyclical data. But, bar charts are more appropriate to show categorical data for comparison. As you see, if you fail to visualize data with the right chart, it can mislead people. Also, wrong chart types make the data more complex. People will fail to understand the data with the inappropriate chart.
Sometimes, data visualizers also use complex chart types for simple data input. It too makes your data visualization boring and inaccurate. Data visualizers might use wrong chart type and color for their target audience.
Wrong chart types interpret the data inaccurately. Also, people might not understand your data visualization. The key to avoid wrong charts for visualizing data are:
If you are unsure about the charts or diagrams to use for data visualization, consult with your senior visualizers. Their insight and suggestions to visualize data with the right chart type will help you.
Data visualizers often neglect the color choices and typography to visualize their data. So, data visualizers end up choosing poor colors, fonts, texts, etc. If the colors aren’t visible and mismatch with the text, data and their visuals become difficult to see. Also, poor color and fonts for the visualized data puts pressure on the audience eye and brain.
The excessive pressure due to poor colors, texts and their combination can disappoint the audience. The color pallets can be confusing too. So, the data analytics and audience can get confused about your colors, texts for significant and less important data.
Data visualizers must know the uses and combinations of proper color patterns and typos. For instance, you could use yellow text over black background to visualize the data. It puts strain on eyes as yellow over black creates deep contrast. Instead, you can use white background and black or light blue text fonts to visualize the numbers in charts, tables, etc., in your data tools.
The best practice for best data visualization with the right color and typography combination are:
For awesome data visualization typography and color schemes, remind me of the suggestions. It is important for data visuals to be visible without any strain.
Data visualization is an art that you need to learn gradually. From selecting the right colors for the numbers, charts, etc., give time to it.
First, understand the audience to whom you will present the data. Is it for big data companies? Is the data visuals for any political parties or businesses? Or, are you presenting the data in educational institutes?
These questions will help you know the audience type for your data visualizations. So, you can choose the right visuals, colors, details and explanations of the data for the audience. It makes your data visuals more attractive.
Data visualization doesn’t need to be complex. Always use simple formats to visualize the data. Don’t include too much information in a single webpage or slide. A simpler data visualization would be better for your audience. If you need to highlight important data, number, location, etc., choose a consistent color scheme.
Before you start visualizing the data, go through it properly. Realize the data, its demography, and data context to organize them. When you have organized the data properly, your visuals will not be cluttered. It also helps you in organizing the data better for quick visualization. It ensures, you use the right chart type and save your time to visualize the data.
Common mistakes when using data visualization tools are avoidable. Stay focused when you work with the data to organize and visualize them appropriately. Also, know your data visualization tool, its dashboard, feature and facility.
With it, you can work faster with your data visualization tools to present the data. Revise the data visuals for any mistakes and correct them for submission. Accurate data visuals with attractive charts, tables, descriptions and organizations will increase your credibility. You could get more data visualization projects, too.
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