Creating data visualization is no cinch. At times, data sets are so large that it’s impossible to perceive something expedient from them. The prime focus of data visualization is to help in identifying patterns and outliers in large data sets. It’s not like designers can take a data set with a large number of entries and create a visualization from scratch; that’s where data visualization tools come in. Data visualization tools are cloud-based applications that help in representing raw data in easy-to-understand graphical formats. These tools provide a simpler way to create visual representations of large data sets. Nowadays, the leading types of data visualization tools on the market have several things in common. Moreover, there are some complex applications available for visualizing data – some have exemplary tutorials and they present it in a way that feels instinctive to the user.
Tableau is generally perceived as the “grandmaster” of data visualization software as it has a huge customer base – 57,000+ accounts across many industries due to its ease-of-use and ability to create interactive visualizations far beyond those provided by general Business Intelligence solutions. It handles the large and very changing datasets, available in Big Data operations which include artificial intelligence (AI) and machine learning (ML) applications. Thanks to its integration with a large number of innovative database solutions like Amazon, AWS, Hadoop, My SQL, and Teradata. Plus, Tableau allows you to create graphics and visualizations in the most effective way possible.
Qlik with its Qlikview tool is another key player here, as well as Tableau’s main competitor. This tool has more than 40,000 customers accounts in almost over 100 countries and those who often use it venerate its customizable setup and wide feature range as its greatest benefit. Yet, this also shows that it takes more time to deal with and use it to its full capability. Moreover, Qlikview offers potent business intelligence, analytics, and enterprise reporting competencies along with a clean and clutter-free user interface.
Qlikview is usually available along with its sister package, Qliksense, which grips data exploration and discovery. There also exists a strong community and many third-party resources available online to provide help to new users and help them understand how to integrate it into their projects.
Hightcharts are available for a free trial for personal use. According to its website, it’s being in use by 72 of the world’s 100 leading companies due to its fast and flexible solution feature, with the least need for specialist data visualization training. The main key to its success is its focus on cross-browser support – this means anyone can view and run its interactive visualizations.
Datawrapper is becoming a popular choice, among media organizations that often use it to make charts and present statistics. Due to its simple and clear interface, it makes it easy to upload CSV data and make straightforward charts and maps that could be available in reports.
Sisense offers a full-stack analytics platform yet, its visualization potential provides an easy-to-use drag & drop interface which enables charts and other complex graphics, along with interactive visualizations available in a hassle-free way. Moreover, it lets many sources of data to gather into one barrier-free and accessed repositories so it can be queried through dashboards and across Big Data-sized sets. After that, dashboards are available for sharing over the organizations, ensuring so that even non-technical staff can find the needed answers to their problems.
There are many tools for data visualization but not every tool is right for every person who seeks to learn visualization techniques, as every tool can’t scale to industry or enterprise purposes. Also, always remember that good data visualization skills will transcend certain tools and products. So, when learning this skill, it’s indispensable to focus on best practices; and explore your unique style when it comes to dashboards and visualization. It’s clear, data visualization isn’t going anywhere any time soon, so building a foundation of storytelling and exploration is important to lead in the tech-world today.