When you have to use graphs or charts to represent your data, do you often get confused about which one to use? or have you ever wondered, you may be using the wrong chart all along and the viewers may not really be understanding the information you’re trying to convey?
Well, you are not alone there and yes, here is a good chance of that being right. Data visualization is not just about converting data into charts and graphs. It is an art of visual presentation of data in a simpler, clear and presentable way to the readers. So when you are to do it, do it the right way. Don’t just plot a pretty graph, present a meaningful visual to the audience.
I’m no data visualization wizard but I sure know a trick or two that will help you choose the right chart and make your visual representation of data better. But before you develop an understanding of choosing the best data visualization type for your data, one needs to understand why choosing the right format is essential.
Studies have shown that humans understand visual data better. Our brains process visual data more accurately than textual data, making it a better format that will enter the reader’s minds. Explaining why many organizations are opting for data visualization. A chart wrongly visualized, does more damage than one thinks. It misrepresents the whole information conveyed and nulls out the whole point of your data. It is not about throwing in many colorful charts in a single box but about how much sense do they make to an onlooker.
Points to be taken care of while choosing an engaging data visualization
- Knowing your audience is the most basic aspect that many forget about while drawing any chart or graph. One should first know more and think about how the audience they are targeting will feel about and grasp the data visuals created. For example, say the targetted audience is children, plotting does complex graphs will number of course will make no sense to them Whereas, if one is targeting scholars of data specialists, more complex structures of graphs can be included.
- Visualize your data with a purpose. When visualizing data keep two things in mind, what are you trying to convey and what do you want the reader to do about the information they have received. Do you want to educate your readers or just inform them about your data? Keep a goal before setting up the work with your data.
- Tell a story with charts. Connect the data points and depict the relationship they share, bring out the important insights and details and highlight the interesting patterns. Talk about the trends over time, the correlation and how the data changes. When you understand your data better, the easier it will get when you lookout for a chart.
- Make sure the chart you use suits the platform you are going to post it on. For example, the data visuals can be for a blog, a newsletter, an e-mail or a website, getting to know the platform beforehand will help with the choice of data visualization type.
Types of Charts and when to use them
Representing it in simple text and numbers
Instead of adding too much data and overloading a chart, represent it in simple text and numbers if you can. Explaining simple numbers or percentage values will make your data more clear, as numbers don’t really turn themselves into charts.
As for labels, describing your data points in a sentence or two is enough. This type of data visualization is apt for usage when dealing with a specific data point or numerical values of data.
Using Bar Charts
These charts are best for comparing data that are in parts and representing a part-to-whole relationship. This chart is best suited for highlighting different categories and showing trends over time.
There are different types of bar charts, namely vertical bar charts that are used when comparing less than 7 categories, horizontal bar charts that are used for comparing 8 or more categories and stacked charts that come into use when dealing with more than one part-to-whole relationships of data.
Bar charts are considered to be the easiest chart to make and understand data. Horizontal bar charts are preferred when dealing with longer labels, as it provides you with more space.
Line Graph and its usage
A line graph is the best use for representing two or more series of data. It helps in their comparison and distinguishing between their characteristics and trends over time.
It is suitable for plotting four lines or less as the more the number of lines is, the more difficult it becomes to distinguish them. Also using different colors for each line will make it easier for the reader to identify them.
Hen to use Area Charts
As similar as they are to other charts since they too determine series and time relationships, they also provide us with the volume of the data. These charts work best with continuous data and are not suitable for discrete data. One can put highly divergent data at the top and low divergent data at the bottom to make it easier for the audience to read the chart.
Things to know about Pie and Donut Charts
They may seem all similar but they are not. Pie charts and Donut charts are as different as a cake and a donut. Pie charts are best suited for broadcasting part-to-whole relations. Donut charts are an improvement or advancement to pie charts, where the total value of data is represented at the center.
These charts are good to use when dealing with small datasets and of no use at all in showing changes or trends over time. These charts are not recommended when visualizing the comparison between data, as the segments of pie may be more that makes it harder to read to an onlooker.
When and why to use a scatter chart
Scatter charts or scatter plots are used to show a correlation between two or more sets of data. This chart is suitable for a humongous amount of data that is difficult to plot on all other graphs.
When the data points are limited, it is better to not plot the data in a scatter chart as there might be no correlation between them at all to show in the chart.
What’s a Bubble Chart
As the name suggests these charts are formed of bubbles and are used for comparing independent values that have easily distinguishable gaps. Bubbles can be of different sizes with respect to the individual value they are representing. However, it is advised to not use other shapes and stick to circles for their representation.
These were some of the universally known types of charts used for data visualization. They might seem tricky or complex at first but once you get the gist of them, they are easy and will make your data look aesthetically presentable and simpler to understand.
