During verification, which question is most relevant?

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Multiple Choice

During verification, which question is most relevant?

Verification focuses on whether the data, after cleansing, still serves the original purpose and goals it was meant to support. The most relevant question is: does the data after cleansing address the original purpose? This checks that the cleansing process hasn’t altered or removed information critical to the task, and that the dataset will actually support the intended analysis, decision, or outcome. For example, if the goal is to run a marketing campaign, you want to ensure the cleaned data still includes the fields and relationships needed for targeting and analysis (such as valid customer IDs, locations, and purchase history) and that those elements align with what the campaign intends to achieve. While other checks matter in data quality, they’re not as central to verification of fit for use. Ensuring there are no syntax errors is typically part of data cleaning and quality checks, not confirming the data meets its purpose. Assessing whether the cleansing introduced bias relates to process risk and fairness, which is important but more about governance and validation. Confirming the data is stored in the correct file format deals with storage and interoperability, not whether it serves the original aim.

Verification focuses on whether the data, after cleansing, still serves the original purpose and goals it was meant to support. The most relevant question is: does the data after cleansing address the original purpose? This checks that the cleansing process hasn’t altered or removed information critical to the task, and that the dataset will actually support the intended analysis, decision, or outcome.

For example, if the goal is to run a marketing campaign, you want to ensure the cleaned data still includes the fields and relationships needed for targeting and analysis (such as valid customer IDs, locations, and purchase history) and that those elements align with what the campaign intends to achieve.

While other checks matter in data quality, they’re not as central to verification of fit for use. Ensuring there are no syntax errors is typically part of data cleaning and quality checks, not confirming the data meets its purpose. Assessing whether the cleansing introduced bias relates to process risk and fairness, which is important but more about governance and validation. Confirming the data is stored in the correct file format deals with storage and interoperability, not whether it serves the original aim.

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