Visual Analytics

What it is, why it matters, and best practices. This guide provides examples and practical advice to help you perform world-class visual analytics of your own.

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What is Visual Analytics?

Visual analytics is the use of sophisticated tools and processes to analyze datasets using visual representations of the data. Visualizing the data in graphs, charts, and maps helps users identify patterns and thereby develop actionable insights. These insights help organizations make better, data-driven decisions.

Visual Analytics Benefits

Sometimes confused with data 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.

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  • Combine data from all your sources

  • Dig into visualizations and dashboards

  • Get AI-generated insights

Visual Analytics Best Practices

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 a data lake or data warehouse.

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

Get Inspired: See the ten most compelling and interesting data visualization examples from recent years.

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Visual Analytics Software Capabilities

With so many vendors offering visualization features as part of their software, it can be difficult to select the right tool to meet your needs. The best data analytics tools include the following capabilities:

Number 1

Integrate and manage big data

Number 2

Allow free exploration

Number 3

Suggest visuals with AI

Number 4

Embed everywhere

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

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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