What Makes a Great Visual Analytics Application?

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The approach to visual stats can be the key impacting factor on whether or not a tool meets your requirements. I feel that aesthetic analytics depends equally on three key attributes.

1. Visualisation

Yes, visualization comes first in the list. Visual analytics without visualization is impossible, so we need to manage to present information in an obvious manner, helping our audience to see the habits in their data somewhat than make a hard work to calculate them.You can also visualize or envision survey data using tableau software.

Just about all of the people aiming to solve the key issues in organizations, are business people – very few are full-time analysts, and few have any significant formal training in reports.

If we start with visuals, users are able to explore the data in a graphical way before trying to evaluate precise values through data tables. Traditional business brains tools – spreadsheets included – start with desks of data and allow the user to make a visual representation here and there.

2. Interactivity

When an inquiring mind is shown with a clear visualization, an improved understanding comes out… nevertheless, the inquiring mind immediately conceives another question which itself can be solved with a different, or altered, visual. If you serve up image after image to allow the user to explore their data as quickly as they think of questions; this high degree of interactivity – almost current in many cases – encourages deep engagement with the data and attracts an individual into a livelier understanding.

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