How I mastered data visualisation with these techniques
A while ago, I wanted to learn how to create stunning data visualisations. I started by reading many articles and watching videos about the subject. After a while, I realised that certain techniques were used repeatedly by the best data visualisers in the field. In this article, I will share the techniques I mastered to create beautiful data visualisations.
Be Clear On The Purpose Of The Visualisation
The first step to creating a great data visualisation is to be clear on the purpose of the visualisation. What are we trying to communicate with our data? What story are we trying to tell? Once we have a clear understanding of the purpose, we can start thinking about the best way to communicate our data.
Let's say we want to create a visualisation that shows the number of sales made by our company each month. The purpose of this visualisation is to show the company's growth over time. With this in mind, mapping how data can be communicated becomes easier.
Will we use a line graph to show the monthly sales over time?
Or will we use a scatter plot to show the relationship between the number of sales and the amount of money spent on marketing each month? The possibilities are endless, but ascertaining the purpose of the visualisation is crucial.
Understand The Data
The next step is to understand the data we will visualise. This means knowing what the data represents and how it is structured. The dataset may contain hundreds of columns, but it's critical to focus only on the relevant data.
For example, if you only want to show the monthly sales for your company, you don't need to worry about the data for the number of employees or the amount of money spent on marketing.
We should also think about the structure of the data. Is it in a format that is easy to work with? For example, if we are using a CSV file, is the data in a single column or multiple columns? If the data is in multiple columns, is it in a format that can be easily joined together?
The last thing to think about is the size of the data. How many rows and columns are in the dataset? Is it too big to work with? If so, we may need to consider using a sample of the data.
Define Our Audience
Before creating the visualisation, we must think about who our audience is. What are their needs? What do they want to see? What level of detail do they need?
The visualisation should be designed to communicate the data in a way that is easy for our audience to understand.
For example, let's say we want to create a visualisation that shows the number of sales made by each salesperson. The audience for this visualisation might be the managers of the sales team.
They will be interested in seeing which salespeople are performing well and which ones are not. They will need to see the data for each salesperson so that they can make decisions about improving the team's performance.
Develop Our Visualisation
Once we have selected the data and defined our audience, it's time to start developing the visualisation. This is where the fun begins!
There are a few things to consider when developing the visualisation. The first is the type of visualisation. There are many different types of visualisations, each with its strengths and weaknesses. We must select the type of visualisation best suited to the data and the story we want to tell.
The second thing to think about is the layout of the visualisation. How are we going to arrange the data? What order should the data be in? How can we make the visualisation easy to understand?
The third thing to consider is the style of the visualisation. What colours and fonts should we use? How can we make the visualisation visually appealing?
Finally, we need to determine the interactivity of the visualisation. What features can we add to the visualisation to make it more engaging?
Test And Improve
After we have developed the visualisation, it's important to test it. Show it to people and see what they think. Ask if they can understand the data or if they find the visualisation visually appealing.
Creating data visualisations can be a lot of fun. But remember that there is a process to follow. We must first select the data, then define our audience, develop the visualisation, and finally test and improve it. By following this process, we can create informative and visually appealing data visualisations.
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