The Effective Of Data Visualization

 

Effective data visualizations

    A data visualization, often known as "data viz," assists analysts to analyze data correctly. A nice way to think about data visualization is that it may be the difference between being completely confused and truly comprehending a problem. Creating great data visualizations is a challenging undertaking; there is a lot of information out there, and understanding it all may be difficult. You will discover some tips and strategies for making successful data visualizations in this article. To begin, you'll go through two frameworks that might help you think about how to structure the data in your visualization. Second, you'll look at pre-attentive qualities and how to leverage them to influence how others perceive your visualizations. You'll go through the design concepts you should keep in mind while designing your visualization. The reading will come to a close with a discussion of some best practices for avoiding misleading or erroneous visualizations.

Frameworks for organizing your thoughts about visualization

Frameworks can help in the organization of your thoughts on data visualization and give you lots of helpful checklists to refer to. Here are two frameworks that you could find useful when you build your own data:

1) The McCandless Method

The McCandless Method identifies four characteristics of effective data visualization:

1. Information: the data you are working with
2. Story: a clear and compelling narrative or concept
3. Goal: a specific objective or function for the visual
4. Visual form: effective use of metaphor or visual expression

Note: One helpful method to approach this framework is to look for areas of the graphic where all four elements overlap partially. A sketch or even art might be a visual form without an aim, plot, or data. Without an aim or function, data plus aesthetic form is just eye candy. It's uninteresting to have data with a purpose but no story or visual representation. To make a successful graphic, all four parts must be present.

2) Kaiser Fung’s Junk Charts Trifecta Checkup

This method is a collection of questions that can assist data visualization consumers evaluate what they're seeing and assessing how successful it is. There are three questions in the Checkup:

• What is the practical question? 
• What does the data say?
• What does the visual say?

Note: Using this checklist, you may consider your data visualization from the perspective of your audience and determine whether or not your graphic successfully communicates your data to them. There are various more building components that might help you develop your data visualizations in addition to these frameworks.