Clarify “clean the data” before writing R
Use business understanding questions to turn a vague data-cleaning request into an R task.
Maya M business partner 1 Define use 2 Constrain rule Maya messages: “Can you clean the customer data before tomorrow?” There are 17 columns with missing values, duplicates, and mismatched IDs. If you guess, every fix becomes a hidden policy decision. The technical move only works if the human request is clarified. 1 ask can become three different analyses CRISP-DM CRISP-DM starts by understanding the business objective because “clean” is not a technical state; it is fitness for a use. In R, cleaning choices encode policy: which duplicate wins, which missing values are tolerable, which IDs define a customer, and…
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