A complete and clear depiction of all relevant data points.

A partial or incomplete presentation of the data.

An overly complex representation that distracts from the main points.

• Large collections of data that contain many interconnected elements and require careful analysis to understand.

Simple and easy-to-understand data sets with few variables.

Data sets that are poorly organized or formatted.

• To improve one's ability to understand and navigate three-dimensional spaces.

To simply present data in a visual format.

To focus only on textual representations of the data.

A user experience that makes them feel like they are in the data.

A basic and uncomplicated presentation of the data.

• An experience that is confusing or stressful for the user.

• To identify relationships and trends within the data that are not immediately obvious.

To simply confirm existing opinions about the data.

To ignore patterns that contradict the intended story.

• The ability to remember and understand intricate details over time.

A reliance on memory to recall data points.

Difficulty in understanding the data in the first place.

To identify repeating themes or changes within the data

To present the data in a way that hides any underlying trends.

• To focus only on individual data points without looking at the bigger picture.

• To move from a basic level of knowledge and achieve a more in-depth understanding of the data.

• To simply accept the data at face value without critical analysis.

To be limited by a pre-existing bias that prevents new insights.

• Pleasing to the eye and aesthetically attractive.

 Prioritizing aesthetics over clarity and functionality in data presentation.

• Using boring or monotonous visuals that aren't able to engage the audience.