It is stated that seeing once is preferable to hearing a hundred times. Today, in the era of big data, when companies are drowning in information from a wide variety of local and cloud sources, this saying is more relevant than ever.
Analyzing information requires more and more effort. Visual aids speed up and simplify this process, and allow you to instantly see the most important things. In addition, most people perceive visual images better than text: 90% of the information that enters the brain is images, so it processes them 60,000 times faster than text1. A compelling argument for visualization tools for data analysis and dissemination, isn’t it?
Data visualization helps to achieve results
The key to advanced analytics is data visualization, which is a crucial aspect of business intelligence products. Visualization helps you evaluate the meaning of information or data being created today. Data visualization refers to the presentation of information in a graphical form, such as a pie chart, graph, or other type of visual representation.
Why Visual Analytics Matters
Good data visualization is critical for data analysis and decision making. Visualization allows you to quickly and easily notice and interpret connections and relationships, as well as identify emerging trends that would not attract attention in the form of raw data. In most cases, graphical representations require no special training to interpret, reducing the chance of misunderstandings.
A well-thought-out graphical presentation not only contains information, but also increases the efficiency of its perception due to visibility, attracting attention and holding interest, unlike tables and documents.
How data visualization works
Most data visualization tools can connect to local or cloud data sources such as relational databases. Thus, data is retrieved for analysis. Users can select the most suited data presentation method from a variety of possibilities. Some tools automatically provide recommendations for using views based on the data being analyzed.
In the past, the easiest and most common way to create data visualizations was to take information from an Excel spreadsheet and convert it into a bar chart, pie chart, or table.
This technique is still extremely powerful, but the art of data visualization has also come a long way in the last 20+ years. There are many tools available to help with data visualization. Charts and graphs make it easy to understand the results, even if you can spot patterns without them.
The graph shows changes over time. The x-axis is usually a period of time, and the y-axis is a quantity.
The histogram also shows changes over time. But if there is more than one variable, a histogram can make it easier to compare the data for each variable.
The bubble chart is depicted as a bubble and is an adaptation of the scatter plot.
A pie chart shows each element as part of a whole and is the best option for illustrating percentages.
The map helps to understand the locality of the subject of study. After all, most data contains a location element.
Infographics require an extensive set of information and provide a comprehensive visual representation. It is great for learning complex and highly subjective topics.
Selecting the most appropriate renderer
The choice of graphical representation is carried out taking into account the type of data and their purpose. Some views are better suited to a particular type of data than others, such as a bar chart or a pie chart. However, most tools offer a wide range of data display options, from simple line and column charts to timelines, maps, relationships, histograms, and custom views.
Ways to use data visualization in business intelligence
Data visualization is by no means a new technique. The ancient drawings on the walls in the Lascaux cave, which illustrated hunting stories, can be considered a type of visual representation.
High technology has added new features, but their goal remains the same – to tell a story.
In business analytics, this could be a story about a company’s performance, told through key metrics. What is the company’s success compared to its competitors? It could be a story about a marketing campaign or an email newsletter: Will you be able to achieve your goals? Or it could be a story about what happens to data sources.
The history can cover the current, past or future time period. The possibilities are unlimited.
Follow this link and learn about data visualization programming languages: https://explain.ninja/blog/6-powerful-programming-languages-for-data-visualization/.