As the name suggests, it helps visualize the relationship between variables in a machine learning model, which makes the variable selection process easier. Data visualization involves the transformation of complex data into simple visual representations in order to facilitate the understanding and exploitation of data.
It is important to remember that the process of data transformation in the field of data visualization is similar to the type of digital data that is to be studied. The processed data can then take on different forms: Chronological representation, pie charts, video animations, computer graphics, and more.
It should also be noted that the data visualization technique has always existed, but it has now taken another dimension with the advances in marketing and the amount of information that needs to be processed.
To practice data analysis by visualization, start by gathering the raw and relevant data about what you want to process. For example, if you manage a B2B company, you can start gathering the demographic data of your prospects or customers. Also, take into account the actions they lead through the web to mention the number of downloads, clicks, or visits. Also, other essential information such as the problems they encounter on a daily basis should be taken into consideration.
Once all of the data has been collected, it can be processed and formatted. This step is important because if the data to be processed is not on the same scale, the visual representations may contain errors. During this operation, it's important to make sure that the important information is put forward so that it is understood at a glance. If data visualization is done properly, the processed data will be easier to understand. The trends will be clearly visible thanks to the use of colors and shapes.
Now that you know what data visualization is, it's also important to know that it contains many technical specifics. To find the best graphical representation, you need to extract the information conveyed through the data and then facilitate communication.
If you want people to pay attention to the information you convey through the encrypted data that has been converted into a chart, you will need to make sure that the chart you have created is aesthetically pleasing. However, do not use graphics that are too flashy, as they may hide the actual information you wish to convey.
Here are some examples of data visualization tools being used today:
Google data studio:
This is a free cloud tool created by Google. With Google Data Studio, you can report from Google suite data (Google ADS for example). Thanks to its connectors and API, it can also manage external data sources.
Periscope Data:
Periscope data is a powerful platform dedicated to Data Analysis. It can gather your company data and create reports. With this tool, you can easily convert your encrypted data into easy-to-understand graphs or reports. It is powerful but expensive.
Chartio:
Chartio was designed to enable entrepreneurs and marketers to exploit data. This tool has a number of connectors, including Microsoft Azure, Google BigQuery, MySQL, Amazon RedShift, CSV Files, Oracle, Google Analytics, Google Sheets and more.
