4 DOs and DON'Ts of Data Visualization

There are massive amounts of data out there. A person creates 1.7 MB of data every second on average. In fact, Google, Facebook, Amazon, and Microsoft store 1,200 petabytes of data. 

With such great numbers, it becomes necessary to represent this data effectively. As a result, business Intelligence Software like Power BI, Tableau, and Qlik Sense have made data visualization more appealing than ever. 

However, there should be some rules that one should adhere to in all this to make more out of your data visualization task.

6 Things to Consider For Data Visualization

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 a job. First, it would be best to make sure that your work adds value, is comprehensible, is easier on the eyes, and conveys the message you want. For that, you need to make sure you answer these questions first:

4 DOs of Data Visualization

1. Do Make It Comprehensive

A beautiful visualization may be as good as the data it portrays; however, it may not be the best. It depends on the data being shown. If the visualization correctly indicates the information in the data, it will likely be effective. 

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.

2. Do Make the Hierarchies Visible

It is a common practice amongst data visualization experts to refrain from ordering the data, which is not necessarily a value-adding thing in terms of scope. However, the data feels more organized and intuitive when ordered alphabetically or in a sequence. 

This makes your data highly captivating. This way, you can get more out of the data than you would otherwise get.

3. Do Keep It Minimalistic

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 significantly less. 

Note that it is common to make mistakes if you create comparison difficulties by adding many colors.

4. Do Ensure the Consistency

It can't be stressed enough that the data should have a standardized color representation and be consistent throughout your analysis.

For example, red should mean the same in all the graphs displayed, and so should orange and so should white. Inconsistent coloring ruins everything for your audience or readers.

4 DON'Ts of Data visualization

1. Don't Use All of Your Data

Before starting your visualization task, you might have a billion petabytes of data. However, summarizing all of it is not the answer. Instead, 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. However, you must first distill your analysis to the point where it becomes more presentable while remaining relevant and exciting. This makes your data concise and understandable.

2. 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. It should only be there if any visual element is contributing to the story.

3. Don't Stick to Only 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 add a monotonous tone to the report.

This neither makes your data visualization clean nor captivating. One size fits all is not the story here, so different charts should be used for other analyses.  

4. Don't Forget to Label the Data Points Clearly

While this might be a contested view, the data should always be labeled and that too clearly and in a standardized way. When making their analysis, many people like purring the labels at the bottom to make the visuals more appealing.

Undoubtedly, this looks good aesthetically, but for a person who is low on time, this becomes a problem to read.

Final Thoughts

An excellent analysis can be distinguished from a dreadfully confused visualization by using interactivity. You must direct the narrative, promote investigation, and, when incorporating interactive elements, ensure that viewers are aware that they can participate in it by, perhaps, providing them with subtly worded directions.

To know more about reaping profits from data, remember to read more about data visualization and analysis on our blog.