Nowadays, data makes up the fabric of anything and everything digital. However, data can be complicated at times, especially for those who try to study and understand it. This is especially a problem for enterprises, because with data growing every single day, visualization is needed.
Data visualization allows data scientists and analysts to study the data in larger quantities then and there. Unfortunately, there are many mistakes to be had, when it comes to data visualization. No matter what tools you use for such a task, mistakes are bound to happen. With that said, here are 5 mistakes to avoid in data visualization.
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1. Not Knowing Your Target Audience
First, your target audience is important. When studying the data, you're looking at a specific demographic. Suppose your target audience is people between the ages of 18 and 25. Where in your data would you find that specific information? And how would you measure that moving forward? With the right tools, you can visualize this data in terms of trends, changes, and so on. Just keep in mind, you'll need to either color-code or indicate this specific data, so that you don't get it confused with the other data. Also, when it comes to your target audience, you have to pick the right one. Otherwise, an audience that you pick won't lead you to the right results.
2. Visualizing Too Much Data
But what if there was too much data? How would you be able to decipher the information that's needed? While data visualization can be very complex, it doesn't have to feel too complex. Therefore, consider doing more than one form of data visualization. In other words, have some data visualized in a certain way, while another group of data is visualized differently, and so on.
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3. Choosing The Incorrect Method
While there are many methods in visualizing data, be sure that you're using the right one. Consider the following methods, and how they operate:
- A pie chart is usually meant to compare different parts of a whole.
- A bar graph is meant to show growth in certain areas.
- A line graph usually shows the growth or decline of something, while tracking its continuing progress.
Remember: There is no one-size-fits-all method. Good data visualization requires good judgment on which method to use.
4. Not Doing The Right Calculations
As for your calculations, you'll need to do the right ones. Otherwise, you'll miscalculate some, or all, of the data that you've visualized. This ties in with the previous mistake, because the right method is required in order for data visualization to work correctly. In this case, make sure that you double check your work after doing your calculations. Remember: Data visualization tools aren't perfect; so, double-checking your work can give you that extra boost in confidence when studying your data.
5. Not Making The Data Reader-Friendly
Finally, think about the people who are going to read your data visualization study. While some readers will have just as much knowledge in data as you do, there are others who don't possess that much knowledge. Wouldn't you want every reader to know what the data is saying? In that case, make your data visualization readable for every single who comes across it. You might need to use more than one method to illustrate what the data is saying, but that's okay. If you use annotations in your data presentation, then that's okay too. The goal here is to make the data reader-friendly.
Conclusion
As you can see, data visualization involves more than just studying loads of information. It takes understanding and tweaking, in order for the data to be comprehensible, presentable, and reader-friendly. While these mistakes are only a handful, there are many other mistakes to be had when attempting to visualize data.
The good news is, you now know what the most common mistakes are, and how to avoid them. It's a good start to avoid these mistakes by recognizing them on the spot, and working towards better solutions, ones that are practical and common sense.
By avoiding the mistakes discussed here, not only will you and others be able to recognize and understand the data, but also deepen your knowledge in data literacy and critical thinking. Remember: Don't be afraid to experiment with different methods, and make sure that you always double check your work, as you study the data.