New word for Data Science – Signuology

I propose a new word for data science for sparking new thinking. Signuology is defined as the study of sets of characteristic predictive signals contained within data in the form of combined features of the data that are characteristic of an observation of interest within the data.

The terms data mining and data structure imply rigid and discrete characteristics. A signal has more flexibility, borrowing from ideas contained in the superposition principle in physics. One can take the same data and ask a difference question, a different dependent variable, and find a different signal; the data structure will be the same. Data structure as a high level concept appears to limit one’s thinking.

Feature engineering is an activity within signuology. These signals allow for a flexibility not afforded in the thinking implied by the terms data structure and data mining.

Allow me to explain the motivation for this new word. During the last data mining project a propensity score was used to sort clients. A percentage of clients with top propensities were clients the business was not interested in. When I removed features with a common thread and still produced good results it dawned on me that the data still had a signal. Multiple signals exist within the data and can be revealed if a different type of observation is chosen as the target or observation of interest to the business.

The signal is only an approximation of the reality one is studying since not all data is captured.

To dispose of rigid thinking I propose a new word, signuology.