How to visualize your data analysis?

Those immersed in the project may not need visualizations to understand those insights, but others will likely struggle without a visual representation. A well-made visualization means more than a thousand words, passing knowledge that would otherwise be challenging to acquire.

Types of data visualizations – introduce various types of data

You can visualize your data in various different ways, depending on its specifics:

  • line charts – perfect to show how things were changing over time, they are usually used to display the evolution of trends.
  • bar charts and column charts – perfect for comparing the trends between different sectors or target groups and breaking down complex topics in a simple manner.
  • scatter charts and bubble charts – the best to show the volume of phenomena or distribution of something and display the impact of different variables on the presented topic
  • maps – perfect for showing issues or phenomena in a geographical context. You can combine them with the scatter method to include volumes, speaking to viewers’ imagination much more than in the case of simple numbers.
  • funnels – perfect for showing the hierarchy and consequential order of the phenomena or subject.
  • timelines – the best tool to show the subject’s distribution in time. You can rely only on the dates or introduce additional methods that suit your subject the best.

Remember that you can blend these methods together for better effectiveness. Data analytics and AI work best with a dose of flexibility. Think about your audience and what can catch their attention. Speak to their aesthetical preferences, instead of only focusing on the content itself.

How to prepare the data for visualization?

To visualize the data effectively, you first need to prepare it. Clean, structured data is essential for effective visualization. How to preprocess it so that it’s ready to be included in the charts?

Define the purpose of your visualization.

That will help you select the right data. Sometimes similar purposes could require entirely different data and display methods. For example: if you want to show the tempo of blockchain adoption in the world, you will likely use a common chart with a timeline, showing the number of companies that have adopted it in different regions over time. If you want to show how blockchain adoption distributes across the world, you can just use a scattered map.

Clean your data

After you select the suitable data for your displayed issue, check it for any duplicate or missing values. If your dataset is missing values, exclude incomplete records or input values. Think about the way you treat outliers – what will be your acceptance/rejection criteria, will you remove it or transform it instead?

Transform and select your data

Your data may need standardization, for instance, if the values come in different units or scales. Decide whether the dataset’s size will affect the visualization’s readability – sometimes, it is worth Thinking about the features that could make your visualization more informative. For instance, numeric data could be much clearer if presented in percentages, allowing you to use the visual method that displays volume like the pie chart.

How to choose the right AI data visualization tool?

There are various visualization tools you can reach out for, from the popular Excel, through Tableau, to Python libraries. Although libraries make it much easier to visualize with Python, you still will need basic coding knowledge. Having an AI data analyst on your board will definitely be an asset if you need to process many insights.

The open, free visual tools like Canva, also will help you show the data in an attractive manner, offering a wide range of premade graphs and charts. However, with coding tools, you will be capable of automating the cleaning and processing of data, as well as the execution of the visualization. Think about your priorities in terms of AI data intelligence and choose the most suitable solution for you!


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