A retail operations team is reviewing feedback from product surveys. They notice some entries have multiple submissions from the same respondent, a subset of phone numbers is stored incorrectly, and many address fields are not populated. What is a core justification for resolving these issues prior to running deeper analytics?
It helps preserve defective records to meet legal guidelines
It improves the quality of analysis
It ensures phone numbers remain unchanged in case of formatting standards differences
It bypasses the need to update any data transformations
Removing duplicated observations, fixing invalid fields, and dealing with incomplete records is important to foster trustworthy analysis. When defective data persists, insights can be skewed and decisions made on flawed assumptions. Some answers point to irrelevant tasks or ignore the impact uncleaned data has on the reliability of results.
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Why is it important to remove duplicate submissions in data analysis?
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What are the implications of having incorrect phone numbers in a dataset?
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How does incomplete data affect the quality of analysis?