A revenue manager receives different sales totals from two independent systems each month. To ensure the final data aligns when preparing a monthly report, which method is preferred for confirming that the values match across both systems?
Inspecting metadata elements to verify field names
Selecting a portion of the data for quick checks
Cross-validation of segments for matching details
Merging all records into one dataset for simplicity
Cross-validation systematically compares multiple datasets or segments to confirm consistent values where needed. This contrasts with choosing a single sample, merging data into one dataset without confirming each record, or just reviewing metadata, none of which verify all subsets for matching details.
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What is cross-validation in data comparison?
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Can you explain how metadata differs from actual data in validation?