Visualization has become an important tool in this new age of Big data wherein billions of raw data generated require to be converted into information that is comprehensible. Data visualization helps in relaying the data information in a more understandable format with a display of trends and outliers. A good visualization is obtained by making a careful balancing between form and function to bring out an effective display of data. This process tells tales by mitigating data glut, and noise, and focusing on the most important facts. The most basic graph could be boring to attract attention or fail to convey a powerful message; the most gorgeous visualization could convey a powerful message.
The art of presenting information and data in the form of diagrams, charts, or maps is known as data visualization. The goal of data visualization is to make it simple for people to grasp their data at a glance – to draw attention to observations that may otherwise go unnoticed when looking at a vertical list of numbers and values.
The goal of a visualization is to explain insights gained from the datasets, illustrate patterns and trends, and make data available to everyone in the organization. Those who can clearly explain an idea, simplify difficult data, and display insights at a glance are the greatest.
There are two basic types of Data visualization - Static Visualization and Interactive Visualization.
Static visualizations are similar to infographics in that they provide a single keyhole perspective of a certain data story.
Interactive visualizations allow you to personalize your story by enabling alternative views of the dataset by sliding a slider or clicking a button.
Again, there are five types of Data visualization categories. Let us discuss them below:
Temporal Data Visualization
The most typical purpose of temporal data visualizations is to show a time series. They are linear and one-dimensional. Temporal visuals have a sense of familiarity to them as well. Newspapers frequently use temporal data visualizations to demonstrate information such as housing market variations from quarter to quarter, or in the company, reports to visualize gains and losses.
The benefit of employing temporal data visualization is that we already know how and when to evaluate them, giving your users an advantage when looking at the data. Some examples are Bar charts, Line charts, Polar area diagrams, Scatter Plotts, Time series Sequences, etc.
Hierarchical Data Visualization:
A collection of objects linked back to a parent item is ordered in a hierarchical data visualization. Hierarchical visualizations are great for displaying a group of data, especially if it comes from a single source. There is a time and place for hierarchical data visualizations as well, as they are more complex and difficult to comprehend, but because of their straight course, it is the easiest hierarchical visualization to follow.
Treemap or diagram, Ring charts, and Sunburst diagrams
Without the use of words, network data visualizations depict relationships between entities—nodes meaning the circles on the visualization, and links which are the lines that connect the nodes.
Some examples of network data visualizations are: Matrix charts, Node-link diagrams, Word clouds, and Alluvial diagrams
Data visualizations with several dimensions are known as multidimensional data visualizations. This sort of visualization tends to be the most bright and eye-catching because of its dimensionality. This is the greatest form of visual to utilize if you want to drill down and filter your data because you can break down your data in a variety of ways to capture the main insights. Some examples of Multidimensional Data visualizations are: Scatter plots, Pie charts, Venn diagrams, Stacked bar graphs, and Histograms
Geospatial visualizations are one of the first types of visualization. They combine recognizable maps with data points. Geospatial data visualizations have a long history as well, having been utilized for navigation before the advent of computer analysis.
Some examples of Geospatial Data visualizations are Flow maps, Density maps, Cartogram, and Heat maps.
The importance of data visualization is seen in every professional field. In fact, it has become almost impossible to envision a professional field that does not benefit from this improvised and advanced understanding of data and data information. Understanding data is beneficial to every STEM profession, and field including the government sector, financial institutions, marketing and manufacturing, education, history, consumer products, sports, and service industries. Given the rate of visualization becoming popular and necessary, those skilled in data visualization and experts in this domain are gaining traction and also becoming a highly valuable talent to learn. Data visualization provides the edge of better communication of information out of the complicated data spectrum.
The more effective graphical communication of an idea/ project regardless of a dashboard or a slide deck, the more effective it is to use data.
There is increasing popularity among people to become data scientists with the world of data growing. There are subsequent changes in acquiring skillsets to accommodate and cater to a data-driven ecosystem. Professionals' ability to use data to make decisions and use graphics to communicate stories about when data informs the people, goal, timing, place, and methods is becoming increasingly valuable.
The new world of professional expanse is now increasingly demanding people who can bridge the gap between technical analysis and creative storytelling which is key in communicating information. This has never been the case with a conventional educational system that had widely made a stark distinction between the two skills.
While traditional education frequently draws a line between creative storytelling and technical analysis, the new professional world values people who can cross the gap: data visualization sits at the intersection of analysis and visual narrative.
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