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.
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.
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.
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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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