Visual abstractions

Visual abstractions

This weeks readings was all about data and the visual presentation of those.

This weeks key learning points:


Quantitative- vs qualitative data

There are two different kind of data. Quantitative and qualitative data. Often quantitative data is collected automatically and in huge numeric masses. It tells "what" is happening. For example how many visitors of your website click a certain button.
On the other hand if you want to find out "why" they press the button, you need user interviews or other methods to collect qualitative data.

Thick- vs big data

Pamela Pavliscak divided data in thick data[^1] and big data:


All the data is worthless if you do not analyze and try to understand it. A good way to do this is through visualization.
Visualization can help you regognizing pattern.

What data visualization can disclose

Guidelines on how to visualize data

Jacques Bertin divides data in three categories. Nominal, ordinal and quantitative data:

This data can be marked with areas, points and lines. To make the marks distinguishable, they can be given properties such as position, color, size, shape, gray and texture.

But not every property works for every data type. Color is not a suitable property to read an exact value from a chart, but it's perfect to differentiate car brands.


[^1] Pavliscak, Pamela. 2015. Data-Informed Product Design. O’Reilly.

Buxton, B. 2007. Sketching User Experiences: Getting the Design Right and the Right Design. Morgan Kaufmann. 76-81.

Eggers William. D., Hamill R., Ali A. 2013. “Data as the new currency. Government’s role in facilitating the exchange”. In Deloitte Review. 13. 18-31.

Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. 2012. Interactions with big data analytics. In Interactions. 19(3). 50-59.

Mackinlay, J. D. & Winslow, K. Designing Great Visualizations. Study for Tableau Software. (undated, retrieved November 2013).