Abstract:

Data visualization is one of the last phases of data chain value, it was born out of the need to meticulously inspect the details and results of the previous phases. It is considered one of the most laborious and tedious tasks in the value chain.

In general, it can be described as the science of analytical reasoning facilitated by static or interactive visual interfaces, to be precise it represents an iterative process that involves the collection of information, the preprocessing of data, the representation of knowledge. The ultimate goal is to better understand the problem at hand. In general, graphs and dashboards are the most used techniques (for decades) in order to synthesize data in a coherent, compact and understandable format, these techniques are used to differentiate the contexts and the intentions of the data such as :
-Description: Attempt to describe and explain the data.
-Report: Summarize past information and events
-Observation: View data to identify patterns that are developing.
-Discovery: Interact with the data to explore and understand the relationship(s) between the data.
However, as the volume of data increases, traditional visualization techniques cannot handle this enormous volume, which has given rise to a new field of advanced visualization which uses interactive methods to represent thousands or even millions of points of data.

To learn more about data visualization, you can read the attached document.