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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VISUAL ANALYTICS GUIDE

What is Visual Analytics?Visual Analytics Benefits Visual Analytics Examples Best Practices Visual Analytics Software Capabilities

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

A Qlik Sense dashboard displaying hospital readmission penalties and rates. Includes a total penalty of $936,453 with breakdowns for AMI, CHF, COPD, HAC, and PNA penalties. Graphs show readmission rates and penalty trends.

Share findings and track progress: Interactive reports and dashboards help users track, organize and share key performance indicators across an organization.
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.
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.
Promote data literacy: Making data easier to work with and understand democratizes data analytics, getting more people across and organization involved.

Visual Analytics Examples

Explore these examples based on KPIs relevant to specific roles.

Marketing

In this example, visual analytics helps a marketer boost ROI by letting them see and understand each phase of the customer life cycle. This is made possible by connecting data from the CRM, advertising tools, and web analytics platform.

More Marketing Examples

Supply Chain

Big data visual analytics can help supply chain managers immediately find connections between complex, disparate data sources by surfacing KPIs and allowing interactive exploration.

More Supply Chain Examples

Sales

Sales managers benefit from the clean, organized presentation of sales data to increase revenue, improve forecasting, and identify key trends.

More Sales Examples

Human Resources

Visualizations can provide HR executives the ability to review and analyze KPIs in a single dashboard. This example shows data on employees by role and by location, plus key data such as gender ratio and training progress.

Explore This Executive HR Example

Qlik Sense dashboard displaying bank margin analysis with metrics on balances, customers, percent margins, and credit scores. Includes various graphs and charts illustrating financial data trends and distributions.

Finance

In this example, a loan manager at a consumer bank can explore how different regions, product and loan officers perform over time and drive the biggest impact on revenue and margins.

More Finance Examples

Business Intelligence Dashboard - IT Dashboard

IT Visual Analytics

IT managers can leverage data analytics and visualization to better anticipate upcoming technology needs and identify underused systems and applications.

More IT Examples

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.

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:

Integrate and manage big data

The best tools can combine and manage data from multiple sources to give users the full picture.

Allow free exploration

Your tool should allow you to freely explore data in whatever direction your intuition leads. Static charts and linear drill-downs make it harder to answer your questions.

Suggest visuals with AI

Augmented analytics recommends visualizations which help even novice users build their own analytics views and discover hidden insights.

Embed everywhere

Your software should let users explore visual data analysis from within whatever websites, portals, apps and business processes they use to make decisions.

FAQs

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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