Do's and Don'ts of Data Visualization

7 Oct · 5 min read

Do's and Don'ts of Data Visualization

The needs of modern businesses have shifted, what used to be paperwork stacked on the top of a warehouse is now one excel sheet. Data has changed and what we do with that data has also changed significantly. Data visualization has been around for years, ever since graphs were used to represent a set of data on a piece of paper, however, things are now changing. There are now tools and techniques that make data visualization more appealing than it ever was. Business Intelligence Software like Power BI, Tableau, and Qlik Sense all of these software’s have changed the way we visualize data and then use it for analysis. However, they are still user-specific software, meaning the visualization is customizable to a very high extent so there should be some rules that one should adhere to in all this to make more out of your data visualization task.

When a data visualization task comes to you there are a few things that you should keep in mind to get the most out of the task. You need to make sure that the work you do adds value and is comprehensible, easier on the eyes, and conveys that message that you want to convey. For that you need to make sure you answer these questions first:

  • Who will be looking at the data visualization?
  • How can I make sure they make sense of the data?
  • What kind of questions they might have?
  • Does it solve all the questions they might have?
  • What am I trying to achieve here?
  • Is it simple enough to comprehend?

Shaffer’s 4 Cs

The best technique or rule to follow in data visualization has got to be the 4 Cs by Shaffer, once you have the objective clear you move on to the visualization part, in which you focus on these 4 aspects.


  • The data should be easily displayed, it can be distinctively seen by the readers
  • Aesthetics are not important as clarity and distinct definition of data points.


  • The data should not have any problems in it, missing values, or junk values.
  • The data should be relevant to the problem being discussed, the visualization tool should also show the clarity of the data.
  • The formatting of the charts, size, and color choice all are important


  • The data visualization should be brief, should not take more time for the reader to read and comprehend, and should never be verbose


  • The story should be told in the most captivating way possible, to engage the readers of the report.

Armed with the knowledge of the 4 Cs of data visualization we will look at the 10 Do’s and Don’ts of Data visualization:

Don't Use all of Your Data:

You might have a billion petabytes of data before you start your visualization task, however, summarizing all of it is not the answer. You need to have a well-presented report at the end of it, you don’t need to clutter it with every piece of analysis you can do on the data. You need to first distill your analysis to the point where it becomes a little more presentable while remaining relevant and interesting. This goes with the Concise part of the 4 C’s.

Do make it as comprehensive:

A beautiful visualization may be as good as the data it is portraying; however, it may not be the best thing. It depends on the data that is being shown, if the visualization correctly shows the information in the data, it is good. So, a visualization not only needs to be easy on the eyes, but it also needs to be informative and correct, thus comprehension of the data should be your priority. It should give all the information while being easy on the eyes.

Don’t Add too many visual elements:

While visual elements do add a bit of character to the analysis it is usually a good practice to remove anything that is not adding any value aside from the visual element. If any visual element is not contributing to the story, it should not be there.

Do make the hierarchies visible:

It is a common practice amongst data visualization experts to not order the data, which is not necessarily a value-adding thing in terms of scope. However, the data feels more organized and intuitive when it is ordered either alphabetically or in a sequence. This adds to the captivating C of visualization, getting more out of the data than what you would otherwise get.

Don’t Only use one type of chart:

You might have seen reports that only use one type of chart, a report full of pie charts or a report full of line graphs. While they might give the information correctly they just add a monotonous tone to the report, which is against the Clean and Captivating C of visualization. One size fits all is not the story here so, different charts should be used to make different analyses.  

Do keep minimum colors:

One of your tasks while making the visualization is to keep misunderstanding on the down low, you need to avoid confusion and too many elements create confusion. Thus the colors in your report should be standardized and very less. It is very common to make mistakes if you create comparison difficulties by adding a lot of colors.

Don’t forget to clearly label the data points:

While this might be a contested view, however, the data should always be labeled and that too clearly and in a standardized way. Many people while making their analysis like purring the labels at the bottom to make the visuals more appealing, which looks good aesthetically but for a person who is low on time, this becomes a problem to read.

Do make the colors consistent:

It can’t be stressed enough that the data should have a standardized color representation and should be consistent throughout the report, red should mean the same in all the graphs displayed so should orange and so should white. Inconsistent coloring just ruins everything for the reader of the report.

Interactivity can make the difference between a horribly-confusing visualization and an all-star analysis. You need to guide the story, encourage exploration, and when building in interactivity, make sure viewers know that they can engage with it — perhaps offering subtle instructions for them.

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