Visual Analytics: What it is and Why it’s Important (2024)

Sometimes confused withdata visualization, visual analytics isn’t simply a matter of representing data graphically. Modern, interactive visual analytics makes it easy to combine data from multiple sources and deeply analyze the data directly within the visualization itself. Plus, AI and machine learning algorithms can offer recommendations to help guide your exploration. Ultimately, visual analytics helps you turn massive data sets into business insights which can have a major positive impact on your organization.

Visualization source:Hospital Readmissions

1. Share findings and track progress: Interactive reports and dashboards help users track, organize and share key performance indicators across an organization.

2. Make faster decisions: Users can understand data insights much more quickly by seeing and working with data sets when they are in a visual format.

3. Explore data more easily: Self-service analytics tools which allow users to interact with data in a visual context allows them to discover hidden relationships and patterns in the data without relying on help from IT.

4. Promote data literacy: Making data easier to work with and understand democratizes data analytics, getting more people across and organization involved.

   See Also

   Data vs. Information: What's the Difference?Data Visualization and Visual Analytics: Seeing the World of Data l SisenseSpotfire | Democratizing Data Understanding: The Role of Information VisualizationImportance, Purpose, and Benefit of Data Visualization Tools!

To help you be successful with visual analytics, we’ve compiled the following key best practices to be aware of.

Define Goals: Before you begin, be sure to define specific goals for your visual data analysis work. What specific questions are you trying to answer?

Integrate and Manage the Data: Your source data needs to be transformed into clean, business-ready information. You’ll need to combine and replicate data from a variety of sources and then bring it into standardized formats stored in a repository such as adata lake or data warehouse.

Simplify Visualizations: Choose the right visual technique to present your story in the simplest way possible. Learn more aboutvisualizing data.

Get Inspired: See the ten most compelling and interestingdata visualization examplesfrom recent years.

What’s the difference between data visualization and visual analytics?

The term data visualization typically 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 refers to the use of an analytics program to perform advanced analysis of complex datasets and allow users to explore and interact with dynamic visualizations.

What are some methods used to visualize data?

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.

Why is visual analytics important?

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.

How do you use visual analytics for business intelligence?

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.

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