7 Oct · 5 min read
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:
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.