Should Visual Dashboard Consumers have Development Control

We need to consider how Information Overload can help impact and drive the development of Visual Dashboards, and how cognitive and data visualization techniques can help guide their design.

As background, there are three main types of working memory:

Intrinsic

  • This refers to the complexity of the material being presented and its effect on cognitive load.

Extraneous

  • This refers to cognitive load that add nothing to the learning experience, such as animations or graphics that are not immediately relevant.

Germane

  • These are the things that assist the student in using their working memory effectively for learning.

Reference: https://www.ispringsolutions.com/blog/cognitive-overload-and-e-learning

Our Information Overload challenge is to offer valuable content for intrinsic memory with navigation helpers (germane memory) while eliminating noise in extraneous memory.

Our tendency is to squeeze into a single dashboard all the useful content for “one stop shopping.”

Stephen Few offers a concise set of dashboard development guidelines that group seven criteria into two general categories:

 

Informative

Criteria that address the degree to which a visualization is informative (i.e., produces understanding

• Usefulness
• Completeness
• Perceptibility
• Truthfulness
• Intuitiveness

Emotive

Criteria that address the degree to which it is emotive (i.e., produces a useful emotional response)

• Aesthetics
• Engagement

Reference: http://www.perceptualedge.com/articles/visual_business_intelligence/data_visualization_effectiveness_profile.pdf 

Thinking beyond Few’s recommendations, today’s application development tools offer an opportunity for dashboard consumers to provide online development guidance for each of their requested dashboards.

Here are some areas to consider in dashboard development:

  • Data item
  • Types of visualizations.
  • Navigation/selection helpers (such as a small clickable (i) above a data item or header to explain what are the choices for viewing and what other dashboard elements will change concurrently–region or time period).

Certainly, dashboard consumers will learn how to custom change the displayed dashboard content (if previously defined as a requested need).

Yet is “trial and error” experimentation the best approach for learning?

Isn’t the consumer’s “time to insight” an important dashboard design criterion?

We already know that Information Overload can lead to lower productivity and greater stress.

Here are some ideas to improve Dashboard Development:

  • Dashboard developers should complete a short course on ways to decrease/avoid dashboard Information Overload.
  • An organization (if sufficiently large) should include an Information Overload champion position that can advise developers how to reduce IO threats among other responsibilities to improve judgment and decision processes.

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