Before she joined LC Labs, was an English teacher. In her classroom, the word “derivative” had a negative connotation. To be derivative was to be overly indebted to another idea and thus to lack ingenuity and creativity.
When applied to datasets, however, “derivatives” abound. In fact, as you’ll see, derivative datasets sometimes serve a critical purpose in making large digital files more available to potential users. In this context, derivative is not a slight. The process of altering a dataset can be essential to a digital project, whether that “data transformation” is standardizing information, removing extraneous information, reformatting, or other tasks.
A new dataset resulting from that data transformation can be considered a “derivative dataset.” The process of alteration changes things about the primary file, including its size, format, and the information it contains. Therefore, it’s important to document any changes you make and always save a new version so you can revert to the original if needed. You’ll also want to keep track of the editorial decisions you made along the way. Summer interns working with the Digital Strategy Directorate explored dataset transformations and their effects in a design sprint this summer–check out their posts on the Signal to learn more about the ways they approached understanding and designing around derivative datasets.