Concluding Remarks

The point of the 4 + 1 model, abstract as it is, is to provide a practical template for strategically planning the various elements of a school of data science. To serve as an effective template, a model must be general. But generality if often purchased at the cost of intuitive understanding. The following caveats may help make sense of the model when considering its usefulness when applied to various concrete activities.

The model describes areas of academic expertise, not objective reality. It is a map of a division of labor writ large. Although each of the areas has clear connections to the others, the question to ask when deciding where an activity belongs is: who would be an expert at doing it? The realms help refine this question: the analytics area, for example, contains people who are good at working with abstract machinery. The four areas have the virtue of isolating intuitively correct communities of expertise. For example, people who are great at data product design may not know the esoteric depths of machine learning, and that adepts at machine learning are not usually experts in understanding human society and normative culture.

Each area in the model contains a collection of subfields that need to be teased out. Some areas will have more subfields than others. Although some areas may be smaller than others in terms of number of experts (faculty) and courses, each area has a major impact on the overall practice of data science and the quality of an academic program’s activities. In addition, these subfields are in an important sense “more real” than the categories. We can imagine them forming a dense network in which the areas define communities with centroids, and which are more interconnected than the clean-cut image of the model implies.

The principal components abstract/concrete and human/machine are meant to help imagine the kinds of activities that belong in each area, through their connotations when combined to form the four bigrams — concrete human, abstract human, concrete machine, and abstract machine. For example, the area of value as the realm of the “concrete human” (or perhaps “concrete humanity”) is meant to connote what the Spanish philosopher Unamuno called the world of “flesh and bone” within which we live and die, that is, where things matter. On the other hand, analytics as the realm of the “abstract machine” is meant to connote the platonic world of mathematical reasoning which, since Euclid, has been characterized by rigorous, abstract, deductive reasoning that has literally been described as an abstract machine (see Alan Turing).

At the center of this model and each area is people. Even in the area classified as “abstract machine,” people and human thinking is at the center.