While reviewing data-usage practices, a data analyst notes that order data were originally collected only for invoicing and tax reporting. A product team now wants to feed the same raw table-which still contains customers' names, email addresses, shipping addresses, and items purchased-into a machine-learning model that will generate personalised recommendations. According to recognised data-usage principles, which action should the analyst take first before providing the dataset to the product team?
Have the product team sign a non-disclosure agreement and share the full dataset unchanged.
Determine whether the new processing purpose is compatible with the original purpose and, if not, obtain a new legal basis or anonymise the data before use.
Drop the email-address field and proceed because internal analytics is automatically permitted.
Copy the table to an encrypted server that only the data-science group can access, then begin model training.
Best practice requires verifying that any new processing of personal data is permissible for the purpose it was originally collected. The analyst must therefore assess whether using the invoicing dataset for a recommendation model is compatible with the initial purpose; if it is not, the organisation must secure a new legal basis (such as fresh customer consent) or sufficiently anonymise the data before proceeding. Simply removing one column, encrypting storage, or having staff sign an NDA addresses security or confidentiality, but it does not resolve the question of lawful or appropriate reuse. Determining purpose compatibility is therefore the necessary first step.
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CompTIA Data+ DA0-002 (V2)
Data Governance
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