The graphical depiction of information and data is known as data visualization. Data visualization tools make it easy to examine and comprehend trends, outliers, and patterns in data by employing visual elements like charts, graphs, and maps. You may have a better understanding of the process by using technology to drill down into charts and graphs for more information, modifying what data you see and how it's handled in real time. When the data is large, such as in a time series, it is a very efficient way of communication. By converting information into a visual context, data visualization makes it easier for the human brain to grasp and extract insights from data, such as a map or graph.

Why is data visualization important?

Data visualization is critical in practically every field. It can be utilized by data scientists who are researching artificial intelligence (AI) developments, sales executives who want to exchange information with stakeholders, or big data projects. Businesses required a way to rapidly and easily acquire an overview of their data as they amassed large amounts of data during the early years of the big data trend. The use of visualization tools was a nice fit.

For similar reasons, advanced analytics is referred known as visualization. When a data scientist uses advanced Predictive Analytics or machine learning (ML) techniques, it's critical to visualize the results in order to track progress and confirm that the models are operating as expected. This is due to the fact that visual representations of complex algorithms are usually easier to understand than numerical outputs.

Data visualization can also assist businesses in determining which factors influence customer behavior, identifying areas that need to be improved or given more attention, making data more memorable for stakeholders, determining when and where specific products should be placed, and forecasting sales volumes.