What is Data?
Data is defined as individual facts, statistics, or items of information. Each piece of data is a unique piece of information. Qualitative or quantitative variables about one or more people, places, or things are known as data in a more technical meaning as they are used in data visualization tools.
What are the Uses of Data?
Data serves as a basis for making informed decisions. It is used to check the quality of critical systems in a company. With the numbers provided, or how graphs are read, it provides measurement how well a particular tactic is working.
Through diagrams, it is easier to pinpoint the root of issues, replicate strengths across the organization, and create baselines and benchmarks so that businesses can have an idea of how to move forward.
Data visualization is the process of creating a visual representation of data. Pie charts, bar charts, histograms, heat maps, etc. are some of the most used data visualization tools. Visual features make it easier to grasp and analyze data, allowing for the extraction of actionable insights from the information.
Thus, data visualization is a powerful tool for making data more accessible to everyone in an organization. Employees can then use data-driven procedures to underpin their actions instead of depending on assumptions, leading to more data-driven processes in the workplace. The media, investors, regulators, and other stakeholders can all benefit from the use of data visualization in their engagement with a company.
The process of creating a data visualization is not always simple. Creating a visualization from scratch from a data set with thousands of entries is not as simple as it sounds. Here come the tools for data visualization.
Data Visualization Tools
A data visualization tool is a form of software that’s designed to visualize large datasets. Each tool’s capabilities vary but, at their most basic, they allow you to input a dataset and visually manipulate it.
Google Data Studio is a powerful, free data visualization tool that is specifically used for creating interactive charts for embedding online. It works with dynamic data pulled from various sources including Salesforce, SQL databases, and Google Sheets. It offers over eighteen types of customizable charts, including bar charts, pie charts, histograms, geo charts, and area charts. Members of the Google community also occasionally generate new charts and share them with other users.
Power BI is available for both on-premise installation and deployment on the cloud infrastructure. Power BI is one of the most complete data visualization tools that supports a myriad of backend databases, including Teradata, Salesforce, Oracle, Google Analytics, Github, Azure, SQL Server, and Excel. The enterprise-level tool creates stunning visualizations and delivers real-time insights for fast decision-making.
One of the most widely used data visualization tools, Tableau, is relatively easy to use and incredibly powerful. The software can integrate with hundreds of sources to import data and output dozens of visualization types—from charts to maps and more.
Data wrapper is a tool that is used to generate charts, maps, and other graphics for use online. The tool’s original intended audience was reporters working on news stories, but any professional responsible for managing a website can find value in it. Featuring a simple and intuitive interface, DataWrapper allows users to create maps and charts that they can easily embed into reports.
Infogram is another popular option that can be used to generate charts, reports, and maps. What sets Infogram apart from the other tools on this list is that you can use it to create infographics (where its name comes from), making it especially popular among creative professionals. Additionally, the tool includes a drag-and-drop editor, which can be helpful for beginners.
You may use many different tools to produce visually appealing data visualizations, and you can learn more about each of them here. When creating visualizations, it is vital to keep in mind that they are only as good as the data they are based on. Misleading visualizations and, even worse, misguided conclusions might result from erroneous data. Business and legal ramifications can follow from this. Data analysis relies on the ability to distinguish between good and bad data.
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About the author: Motunrayo Ademoroti is regarded as one of the most capable data analysts in WayMaker Digital. Because of her hard interest in graphic work, she decided to go on a professional path that makes the most of her meticulous nature. Aside from her interest in technology, the majority of her interests include activities such as reading, dancing, and listening to music.