A software development team merges several datasets from different departments to create a final dashboard. They notice the number of rows is lower than expected. Which action would help confirm the final numbers are reliable prior to publication?
Check for duplicate entries across the unified dataset
Review aggregation methods used during merging processes
Implement cardinality constraints across related tables
Perform cross-validation of the final dataset with the original sources
Performing cross-validation helps spot discrepancies by comparing the final dataset against the original sources or other references. This confirms that the quantity and content match anticipated values. Examining aggregation methods or checking for duplicate entries may suggest potential problem areas but do not directly confirm the overall accuracy of the merged data.
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What is cross-validation in the context of datasets?
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Why is it important to review aggregation methods used during merging processes?
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What are cardinality constraints, and why are they relevant in data merging?