In 1961, a clever science fiction author named Robert Heinlein coined a new term in one of his novels. The term was “grok.” Grok is a wonderful word that means "to empathize or communicate sympathetically; also, to experience enjoyment,” and is a term similar to the later sixties phrase “dig it.” Grok meanings also include “to drink,” “to love,” and “to be one with.” Grok can apply when one experiences those moments of insight as a new realization coalesces in one’s mind, or as that joyful experience continues to provide a thrill. Perhaps grok can be a mental state one achieves as one attains the peacefulness advertised in another 1960s term—“be here now.” Grokking can be vital in working through the creation of a new logical data model. If one does not understand the data, then one cannot model the data. Alternately, if one groks the data, the data modeling efforts will flow freely.
How does one go about grokking data? Individual data modelers may have their own routines when learning new subject areas, but everything starts with discussion. Grab a subject matter expert and start asking questions to learn what is going on across the subject area. This is where a new data modeler can start channeling their inner Clarence Darrow. It is not unusual for a data modeler to be compared to a lawyer going through the exercise of cross-examining a witness. What does this term mean? How does that process work? Gathering a complete picture of the area in focus is required. Subject matter experts are often busy people with full schedules, and getting time with them may be limited. Beyond one-on-one time with an expert, other documentation may exist to supplement one’s knowledge. Corporate standards, training materials, presentations, reports. . . as well as that 1000-pound gorilla sitting on the desk known as the internet. While some things published on the web may be suspect, there is much information easily available. As one gathers information, especially in an area that may be new to them, another fun tool to use can be a mind map, i.e., putting together a graphic, perhaps snagging useful pictures of relevant objects, buildings, animals, whatever, then connecting them with arrows, lines, dots, annotations, questions. . . and don’t be stingy with using colors. Everybody loves diagrams loaded with colors. Time with the subject matter expert can be useful going over and refining one’s mind map, fleshing out details or correcting misunderstandings. One’s mind map can serve as a mandala of sorts to focus on the subject area and get to a deeper level of understanding, to grok. One of the meanings of grok is “to be one with,” which can evoke images of Chevy Chase saying, “be the ball” in Caddyshack. And if you have that going for you, that can be nice.
As one embraces the mind map, the next step is to consider the data needed to support the subject area in question and start imagining what shape the data must look like in order to be functional. There is a connection within this data mind map context, in that form and function do complement each other. Here is where the imagination and creativity of the data modeler can blossom and take over. At times this process can seem very magical, but truthfully, the leap is really the application of rationality and logic. If one is successful in exploring, the data modeler can see and understand, or even predict, the pain points proffered by the SME. Maybe the data modeler can even offer expansive options that might be seen as improvements yet to be requested. To normalize a set of data properly, one must grok the data. Go forth as a data architect, data modeler, or whatever title your organization embraces, and grok away. Gunga galunga.