Drucker -- Chapter Two

In this chapter, Drucker builds on a selection of ideas she presented in her Introduction and Chapter One. She begins the Introduction by comparing two images, one which is a “representation of knowledge” and another which is a “knowledge generator.” She begins Chapter Two by discussing this distinction in more detail. Toward that end, she provides numerous historical examples of “knowledge generators” and explains how each accomplishes that as opposed to simply displaying information.

At the core of her idea of a “knowledge generator,” however, is the act of interpretation, or, at the very least, some sort of “reader” involvement. She explains, for example, how an object like a train time table (or a map, for the same reason) generates knowledge because it makes combinatoric calculation possible, it displays information that allows a reader to chart an individualized itinerary, thereby creating new knowledge not explicitly displayed. She also explains how even these static objects, which see, to display objective data, are really just objects of interpretation of their creators. A world map, for example, necessitates the projection of the three-dimensional spherical world onto a two-dimensional flat plane. Such a mapping necessarily creates some sort of aberration in the final two-dimensional product, and the choice of aberration is the interpretive act of its creator. She mentions the commonly used Mercator projection, which preserves the relative arrangement of landmasses but distorts their size near the Earth’s poles. Reading such a map as an objective depiction of the world without considering the interpretation involved in its origin yields an ultimately false, distorted view of the world.

Taking this idea of interpretation further, Drucker explicates the common conflation of observed data and the phenomenon observed to generate that data. Describing data as an objective depiction of its generating phenomenon erases the idea that there is always a process of observation in collecting that data. Observation involves human intervention, and as such, it is necessarily an act of interpretation — the resulting data, then, cannot be objective; it is a result of (inherently subjective) interpretation. To remedy this issue, and to create perhaps more powerful visualizations, Drucker begins describing a method of creating visualizations that make the involvement of interpretation — or at least its related uncertainty — evident. She advocates for the removal of discreet, continuous, clear-cut coordinate system on a graph, for example, in favor of a system that has discontinuities and expresses underlying uncertainty. She sees the act of interpretation not as something that is completely incomprehensible in logical terms: describing it as “stochastic and probabilistic,” she claims that I can be modeled by the same (complicated) “mathematical and computational models as other complex systems.”