Making Sense of Others’ Data Structures by Eruditio Loginquitas.
From the post:
Coming in as an outsider to others’ research always requires an investment of time and patience. After all, how others conceptualize their fields, and how they structure their questions and their probes, and how they collect information, and then how they represent their data all reflect their understandings, their theoretical and analytical approaches, their professional training, and their interests. When professionals collaborate, they will approach a confluence of understandings and move together in a semi-united way. Individual researchers—not so much. But either way, for an outsider, there will have to be some adjustment to understand the research and data. Professional researchers strive to control for error and noise at every stage of the research: the hypothesis, literature review, design, execution, publishing, and presentation.
Coming into a project after the data has been collected and stored in Excel spreadsheets means that the learning curve is high in yet another way: data structures. While the spreadsheet itself seems pretty constrained and defined, there is no foregone conclusion that people will necessarily represent their data a particular way.
Data structures as subjects. What a concept! 😉
Data structures, contrary to some, are not self-evident or self-documenting.
Not to mention that like ourselves, are in a constant state of evolution as our understanding or perception of data changes.
Mine is not the counsel of despair, but of encouragement to consider the costs/benefits of capturing data structure subject identities just as more traditional subjects.
It may be costs or other constraints prevent such capture but you may also miss benefits if you don’t ask.
How much did it cost for each transition in episodic data governance efforts to re-establish data structure subject identities?
Could be that more money spent now would get an enterprise off the perpetual cycle of data governance.