Data visualization tools are data processing tools that go a long way in enabling the interpretation of complex data by transforming raw data into graphical illustrations that are easier to understand so that users can quickly discern new ideas and knowledge from the data. The concept of large numbers and rows of numbers is better demonstrated in diagrams and charts to make patterns, trends, and anomalies easier for researchers to identify and evaluate. This is especially true in cases of multidimensional and time-series data where the traditional text-based or tabular view has failed to deliver critical results and therefore hinders the prediction or learning effectively. Even more interesting is that a modern Data visualization tool can make the trend or pattern of data interactive, meaning that one can view what he or she thinks is necessary, and as many details as possible can be included in the graphical illustration.

This interactivity provides a platform for exploring data further, making it easier for researchers to zoom in, drill down, filter, and extract data to fit more than one hypothesis and to meet the goal that one has. While this is happening, there is a higher likelihood that the user will get more ideas than what he or she would have noted on a tabular form of data as data visualization encourages teamwork and collaboration.

Modern ways of visualizing data are amazing and allow insights in big data that cannot be achieved with text and tables. Nonetheless, I want to be a bit of a devil's advocate.

The biggest advantage of visual data, that you can get information by just taking a glance, also bears a risk - it makes it easy to skew data by playing with the makeup of a figure. Take a simple bar chart for example: it may show a huge difference between two samples, but taking a closer look at the axis, it reveals the real difference is close to 1 %. This is worse with broken up axes. I have often seen manipulative figures like this in political articles.

Also think about colors. They have significant suggestive power. Intentional or not, with the palette you choose, you might already say what is "good" and what is "bad".

There are more considerations like that, but the main argument is: Visualization is great, but doing it right is a skill (or more so an art) by itself.

Great comment, Stefan Kranz! This reminds me of a joke on how the "jet rainbow colour-map poses a health threat" (1 minute explanation of the issue: https://youtu.be/xAoljeRJ3lU?t=62). Perceptual uniformness of a colourmap is in fact very important.

Another short case about the problem can be read here (https://blogs.mathworks.com/headlines/2018/10/10/a-dangerous-rainbow-why-colormaps-matter/), and the solution re-posted here (https://medvis.org/2016/02/23/better-than-the-rainbow-the-matplotlib-alternative-colormaps/).