importance of data visualisation
It has been said that “a picture is worth a thousand words.” And today, in the era of big data, when businesses are inundated with information from varied data types and from on-premises and cloud-based sources, that old saying has never been more relevant. Sifting through information to understand what matters and what doesn’t is becoming more difficult. Visuals make analysis much easier and faster, and offer the ability to see at a glance what matters. What’s more, most people respond far better to visuals than text—90 percent of the information sent to the brain is visual, and the brain processes visuals at 60,000 times the speed of text1. Those points make a strong case for the use of data visualization for analyzing and conveying information.
Data visualization is part of many business-intelligence tools and key to advanced analytics. It helps people make sense of all the information, or data, generated today. With data visualization, information is represented in graphical form, as a pie chart, graph, or another type of visual presentation. Good data visualization is essential for analyzing data and making decisions based on that data. It allows people to quickly and easily see and understand patterns and relationships and spot emerging trends that might go unnoticed with just a table or spreadsheet of raw numbers. And in most cases, no specialized training is required to interpret what’s presented in the graphics, enabling universal understanding.
A well-designed graphic can not only provide information, but also heighten the impact of that information with a strong presentation, attracting attention and holding interest as no table or spreadsheet can.
Data visualization means drawing graphic displays to show data. Sometimes every data point is drawn, as in a scatterplot, sometimes statistical summaries may be shown, as in a histogram. The displays are mainly descriptive, concentrating on ‘raw’ data and simple summaries. They can include displays of transformed data, sometimes based on complicated transformations. One person’s statistics may be another person’s raw data. As with other aspects of working with graphics, it would be useful to have an agreed base of concepts and terminology to build on. The main goal is to visualize data and statistics, interpreting the displays to gain information.
Data visualization is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining to check data quality and to help analysts become familiar with the structure and features of the data before them. This is a part of data analysis that is underplayed in textbooks, yet ever-present in actual investigations. Look, for instance, at the one-sided peaks in the distributions of marathon finishing times (marastats, 2019).
Graphics reveal data features that statistics and models may miss: unusual distributions of data, local patterns, clusterings, gaps, missing values, evidence of rounding or heaping, implicit boundaries, outliers, and so on. Graphics raise questions that stimulate research and suggest ideas. It sounds easy. In fact, interpreting graphics needs experience to identify potentially interesting features and statistical nous to guard against the dangers of over interpretation. Just as graphics are useful for checking model results, models are useful for checking ideas derived from graphics
Data visualization is a key component of the big data revolution. Big data refers to a massive volume of data that can be difficult for individuals and organizations to store, manage, and analyze. Data visualization helps people make sense of all this information, or data. Data is typically viewed as a spreadsheet of lots of numbers, but it can also be represented as a graph or as some other type of visual presentation. Good data visualization is critical for converting piles of numbers into information that quickly enables people to see and understand patterns and relationships and spot emerging trends.
The term “data visualization” is used to describe visual representations of abstract or concrete data, such as statistical or network data. Data visualization involves choosing and designing graphics (including charts, maps, diagrams, and images) that reveal patterns and trends of the underlying data. This is different from traditional graphing methods such as histograms and pie charts that organize raw numerical data into an easy-to-understand format.
Check Amazon Data Visualization Books
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