A picture is worth a thousand words, as the saying goes. Visuals are easy to grasp, providing us with powerful narratives that we can process instantly. They can be used to transform data into a format that is easier for us to explore, understand, and analyze. Visual analytics help non-data scientists interact with data more easily and freely. Through visual analytics, modern business analytics software enables end users of all skill levels to find business-critical insights in enterprise data. Even when faced with massive, dynamic, multi-dimensional datasets, users can leverage visual analytics to discover, engage with, and derive actionable insights from the patterns, relationships, and trends in their data.
Sometimes confused with data visualization, visual analytics isn’t simply a matter of representing data graphically. Visual analytics integrates computational analysis techniques with interactive visualizations, offering users a new and innovative way to interact with, explore, and manipulate data. Data analytics* software employing AI and machine learning algorithms is used to organize, cleanse, and analyze the data—and offer insights to help guide the user’s exploration. Users spend less time on data preparation and analysis and more time examining and interpreting the patterns and relationships exposed.
A modern version of reporting analytics, visual analytics provides the user with interactive graphical displays of data that not only accelerate insight discovery but also support the presentation and dissemination of their findings to other stakeholders. Visual analytics helps users dive deeper into their data by letting them interact directly with the data analysis capabilities of computers. By combining their own analytical reasoning abilities and intuition with the storage and processing capabilities of today’s computers, humans can obtain rapid insight into complex issues, situations, and scenarios.
Used to synthesize large amounts of information and obtain critical insights to support different types of assessment, planning, and decision-making, visual analytics is now employed across all major industries, including financial services, high tech, retail, healthcare, transportation, and many others.
*What is data analytics? It refers to the process of or the use of technology for examining, identifying patterns and trends in, interpreting, and deriving meaningful insights from data.
Visual analytics is a powerful capability now supported by industry-leading data analytics and BI solutions. More vendors are choosing to incorporate visual analytics into their toolsets because it allows a wider range of business users to explore and find insights in large datasets. Users that don’t have coding or data science skills can feel comfortable working with and using data to support their own projects and initiatives.
Visual analytics helps to encourage broad use of data analytics and business intelligence software across the organization. It streamlines and accelerates the process of data discovery while giving users the freedom to blaze their own path through data. They can use their knowledge, intuition, and the advanced analytics capabilities of software to solve problems, develop new strategies, and support the business more effectively.
A key component of modern BI solutions, visual analytics doesn’t simply democratize data. It democratizes data analytics, allowing more users than ever before to manipulate and explore more types of data and helps firms better take on the velocity, volume, and varieties of data flowing into their organization. Visual analytics is critical for big data analysis initiatives because it allows citizen data analysts to quickly sort through and extract insights from vast amounts of multi-sourced data. Business users who might have previously shied away from data analytics can now use it to help them make transformative discoveries, work smarter, and act faster.
Built on a unique Associative Engine and powered by an AI-fueled Cognitive Engine, Qlik Sense is a robust BI and analytics platform that empowers everyone in your organization to combine, visualize, explore, and extract surprising insights from your datasets with drag-and-drop simplicity. By indexing and understanding all relationships in your data, Qlik Sense surfaces patterns,trends and outliers your users might otherwise miss. Visual analytics capabilities allow for smart, agile, interactive data discovery and reporting, letting users dive in and explore in any direction—searching, selecting, drilling down, zooming out, or changing perspective altogether to better understand your data. And with AI-powered conversational analytics, they can even ask Qlik Sense questions, letting the program spin up useful graphics and relevant insights. Qlik Sense supports open-ended, curiosity-driven exploration for everyone—no matter their skill set.
Data visualization refers to the graphical depiction of data, or representing data in bubble charts, heat maps, and other visuals to help people better understand the patterns, relationships, trends, and other meaningful insights in datasets. Visual analytics, on the other hand, refers to the use of an analytics program that shows users the results of complex algorithmic processing of their datasets. Users can explore and interact with dynamic data visualizations, an iterative process that helps users engage more easily and deeply with data in a visual way.
Bar charts, line charts, pie charts, cartograms, donut charts, heat maps, histograms, radial maps, streamgraphs, scatter plots, timelines, treemaps, and word clouds are some of the many ways data can be presented visually.
It is important because it allows users without data science skills or experience to combine, manipulate, and explore large, dynamic, multi-dimensional, and multi-sourced datasets. In a world in which big data becomes the norm, visual analytics becomes an essential tool.
Business users can leverage visual analytics to more easily explore and understand large volumes of diverse data and gather the insights they need to solve problems, identify key opportunities, optimize performance, and inform strategic and tactical decision-making. It can also be used to create interactive dashboards and support collaboration amongst teams working with the same datasets.