One of the most relevant aspects of understanding the rise of Visualization today is, without a doubt, the existence of new digital tools for creating graphics. The availability of digital communication networks such as the internet and the omnipresence of topics such as artificial intelligence and data science that extensively use visualization strategies has allowed many people to know and start using these tools.
Another critical factor in this proliferation of visualization tools is the emergence of numerous communities interested in the subject, which share their developments and achievements in a distributed way, constantly optimizing these tools, which results in an ever-greater simplicity and ease of use.
The range goes from tools that allow you to capture, manage and transform data to those that help present and disseminate information through static or interactive graphics. There is a vast range of possibilities for the different stages of the visualization process and the different goals that we have in mind. So, the question in what is data visualization tool? is which of these tools is more convenient in one case or the other. Let’s see:
1. According To Our Objectives: Analyze Or Communicate
A project that involves visualizations can be considered from two perspectives: Visualization for analysis and Visualization for communication.
A. Visualization for analysis: Oriented to understand what is in the data. Visualization is used to represent their structures.
John Tukey (the USA, 1915 – 2000) greatly privileged the visual analysis of data. He developed the Exploratory Data Analysis (EDA), which aimed to generate visual explorations in the data and let them tell us about their structure and patterns.
In this case, tools such as RStudio or Python offer a wide range of options but require specific technical knowledge of some complexity to use. This is the domain of data scientists and statisticians, among others.
b. Visualization for communication: On the other hand, we can know the information well, understand what the patterns of the data are, but we need to communicate the conclusions efficiently, or we must target a specific audience, so it is necessary to translate the complexity into a more empathetic language that facilitates understanding.
Of course, these two significant areas of analytics and communication can converge at some point. Therefore, it is perfectly possible to develop projects with an interface that allows the end-user to freely explore in search of patterns in the data, according to their interests and questions.
2. According To The Complexity Of The Tool: Standardized Graphic Tools
Another way to choose tools is because of their difficulty or ease of use, if they are intuitive and quick to learn, or require a certain level of technical knowledge. Of course, choosing an unwieldy tool offers flexibility, but it can involve extensive learning. We can distinguish then three areas: graphical tools, ready-made visualizations, and programming languages.
A. Graphic tools: We can find image editing programs that specialize in the creation of vectors or bitmaps and that are mainly used by designers, artists, journalists, photographers, among others.
However, despite being very intuitive, they require a lot of effort to produce statistical graphics. The relationship between the value of the data and its representation is not always consistent. They are tools where we could say everything is done “by hand.” We find tools such as Photoshop or Illustrator (from Adobe) or Gimp and Inkscape (FLOSS) in this category. They are widely used for journalistic infographics.
b. Pre-made visualizations: Some tools allow you to enter the data and select the representation from various options (galleries with visualization “templates”). You are used by data scientists, programmers, and even non-experts looking for speed in trying different alternatives for representation.
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